Vehicular vision system
A vehicular vision system includes a processor for processing image data captured by first, second and third cameras disposed at the left side, right side and rear portion, respectively, of the vehicle. The field of view of the first camera overlaps with the field of view of the rear backup camera, and the field of view of the second camera overlaps with the field of view of the rear backup camera. At least in part responsive to processing captured image data, the control outputs a composite image for display at a display disposed in an interior cabin of the vehicle, and determines that a camera is misaligned due to a shift in at least one of yaw, pitch and roll alignment of one of the cameras disposed at the vehicle and, responsive to the determination of misalignment of that camera, at least partially compensates for the misalignment of that camera.
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The present application is a continuation of U.S. patent application Ser. No. 14/946,855, filed Nov. 20, 2015, now U.S. Pat. No. 9,940,528, which is a continuation of U.S. patent application Ser. No. 14/690,745, filed Apr. 20, 2015, now U.S. Pat. No. 9,193,303, which is a continuation of U.S. patent application Ser. No. 14/033,962, filed Sep. 23, 2013, now U.S. Pat. No. 9,014,904, which is a continuation of U.S. patent application Ser. No. 13/776,100, filed Feb. 25, 2013, now U.S. Pat. No. 8,543,277, which is a continuation of U.S. patent application Ser. No. 13/566,266, filed Aug. 3, 2012, now U.S. Pat. No. 8,386,114, which is a continuation of U.S. patent application Ser. No. 13/370,694, filed Feb. 10, 2012, now U.S. Pat. No. 8,239,086, which is a continuation of U.S. patent application Ser. No. 13/190,931, filed Jul. 26, 2011, now U.S. Pat. No. 8,116,929, which is a continuation of U.S. patent application Ser. No. 12/979,497, filed Dec. 28, 2010, now U.S. Pat. No. 7,991,522, which is a continuation of U.S. patent application Ser. No. 12/764,355, filed Apr. 21, 2010, now U.S. Pat. No. 7,877,175, which is a continuation of U.S. patent application Ser. No. 11/315,675, filed Dec. 22, 2005, now U.S. Pat. No. 7,720,580, which claims benefit of U.S. provisional application, Ser. No. 60/638,687, filed Dec. 23, 2004, which is hereby incorporated herein by reference in its entirety.
FIELD OF THE INVENTIONThe present invention relates generally to vision or imaging systems for vehicles and is related to object detection systems and, more particularly, to imaging systems which are operable to determine if a vehicle or object of interest is adjacent to, forward of or rearward of the subject vehicle to assist the driver in changing lanes or parking the vehicle. The present invention also relates generally to a lane departure warning system for a vehicle
BACKGROUND OF THE INVENTIONMany lane change aid/side object detection/lane departure warning devices or systems and the like have been proposed which are operable to detect a vehicle or other object that is present next to, ahead of or rearward of the equipped vehicle or in an adjacent lane with respect to the equipped vehicle. Such systems typically utilize statistical methodologies to statistically analyze the images captured by a camera or sensor at the vehicle to estimate whether a vehicle or other object is adjacent to the equipped vehicle. Because such systems typically use statistical methodologies to determine a likelihood or probability that a detected object is a vehicle, and for other reasons, the systems may generate false positive detections, where the system indicates that a vehicle is adjacent to, forward of or rearward of the subject vehicle when there is no vehicle adjacent to, forward of or rearward of the subject vehicle, or false negative detections, where the system, for example, indicates that there is no vehicle adjacent to the subject vehicle when there actually is a vehicle in the adjacent lane.
Such known and proposed systems are operable to statistically analyze substantially all of the pixels in a pixelated image as captured by a pixelated image capture device or camera. Also, such systems may utilize algorithmic means, such as flow algorithms or the like, to track substantially each pixel or most portions of the image to determine how substantially each pixel or most portions of the image has changed from one frame to the next. Such frame by frame flow algorithms and systems may not be able to track a vehicle which is moving at generally the same speed as the equipped vehicle, because there may be little or no relative movement between the vehicles and, consequently, little or no change from one frame to the next. Because the systems may thus substantially continuously analyze substantially every pixel for substantially every frame captured and track such pixels and frames from one frame to the next, such systems may require expensive processing controls and computationally expensive software to continuously handle and process substantially all of the data from substantially all of the pixels in substantially each captured image or frame.
Many automotive lane departure warning (LDW) systems (also known as run off road warning systems) are being developed and implemented on vehicles today. These systems warn a driver of a vehicle when their vehicle crosses the road's land markings or when there is a clear trajectory indicating they will imminently do so. The warnings are typically not activated if the corresponding turn signal is on, as this implies the driver intends to make a lane change maneuver. Additionally, the warning systems may be deactivated below a certain vehicle speed. The driver interface for these systems may be in the form of a visual warning (such as an indicator light) and/or an audible warning (typically a rumble strip sound). One application warns a driver with an indicator light if the vehicle tire is crossing the lane marker and no other vehicle is detected in the driver's corresponding blind spot; and/or further warns the driver with an audible warning if the vehicle is crossing into the adjacent lane and there is a vehicle detected in the driver's blind spot.
There is concern that the current systems will be more of a driver annoyance or distraction than will be acceptable by the consumer market. Using the turn signal as the principle means of establishing to the warning system that the maneuver is intentional does not reflect typical driving patterns and, thus, many intended maneuvers will cause a warning. As a driver gets annoyed by warnings during intended maneuvers, the driver will likely begin to ignore the warnings, which may result in an accident when the warning is appropriate.
Therefore, there is a need in the art for an object detection system, such as a blind spot detection system or lane change assist system or lane departure warning system or the like, which overcomes the short comings of the prior art.
SUMMARY OF THE INVENTIONThe present invention is intended to provide an object detection system, such as a blind spot detection system, a lane change assist or aid system or device, a lane departure warning system, a side object detection system, a reverse park aid system, a forward park aid system, a forward, sideward or rearward collision avoidance system, an adaptive cruise control system, a passive steering system or the like, which is operable to detect and/or identify a vehicle or other object of interest at the side, front or rear of the vehicle equipped with the object detection system. The object detection system of the present invention, such as for a lane change assist system, utilizes an edge detection algorithm to detect edges of objects in the captured images and determines if a vehicle is present in a lane adjacent to the equipped or subject vehicle in response to various characteristics of the detected edges, such as the size, location, distance, intensity, relative speed and/or the like. The system processes a subset of the image data captured which is representative of a target zone or area of interest of the scene within the field of view of the imaging system where a vehicle or object of interest is likely to be present. The system processes the detected edges within the image data subset to determine if they correspond with physical characteristics of vehicles and other objects to determine whether the detected edge or edges is/are part of a vehicle or a significant edge or object at or toward the subject vehicle. The system utilizes various filtering mechanisms, such as algorithms executed in software by a system microprocessor, to substantially eliminate or substantially ignore edges or pixels that are not or cannot be indicative of a vehicle or significant object to reduce the processing requirements and to reduce the possibility of false positive signals.
The object detection system of the present invention may capture images at a side of the vehicle and may process various windows of the images to detect a vehicle in the adjacent lane or other object, such as a bicycle, in the adjacent lane. The system may adjust the image processing to account for misalignment of the camera at the side of the vehicle. The system may adjust the area or zone of interest in response to a turning of the vehicle's wheels, such as when the vehicle is turning or curving along a curve in the road. The system may distinguish between vehicles or other objects and shadows of objects/vehicles so that a shadow of a vehicle two lanes over may not be considered a vehicle in the adjacent lane. The system may switch between daytime and nighttime algorithms and may be operable to detect headlamps of vehicles in the adjacent lane.
According to an aspect of the present invention, an imaging system for a vehicle includes an imaging array sensor and a control. The image array sensor comprises a plurality of photo-sensing pixels and is positioned at the vehicle with a field of view exteriorly of the vehicle. The imaging array sensor is operable to capture an image of a scene occurring exteriorly of the vehicle. The captured image comprises an image data set representative of the exterior scene. The control algorithmically processes the image data set to a reduced image data set of the image data set. The control processes the reduced image data set to extract information from the reduced image data set. The control selects the reduced image data set based on a steering angle of the vehicle.
Optionally, the control may process the reduced image data set with an edge detection algorithm to extract information from the reduced image data set. The image sensor may be one of (a) part of an exterior rearview mirror assembly of the vehicle and with a field of view at least partially sideward of the vehicle, and (b) at an upper windshield area and behind the windshield of the vehicle and with a field of view forward and through the windshield (such as at an area that is cleaned by the windshield wiper or wipers of the vehicle when the windshield wipers are activated). Optionally, the image sensor may be part of an exterior rearview mirror assembly of the vehicle and with a field of view at least partially sideward of the vehicle, wherein the imaging system comprises a side object detection system for detecting objects at a side of the vehicle. Optionally, the image sensor may be at an upper windshield area and behind the windshield of the vehicle and with a field of view forward and through the windshield, wherein the imaging system comprises a lane departure warning system.
Therefore, the present invention provides an imaging system for use as or in association with a side object detection system and/or a lane departure warning system. The system is operable to process captured image data of a scene occurring exteriorly and along one or both sides of the vehicle to determine if a target vehicle or object of interest is located at or in the lane adjacent to the subject or host vehicle. The imaging system of the present invention may process zones or areas of interest in the captured images and may adjust processing to accommodate any misalignment of the camera that may occur during installation of the camera at the side of the vehicle. The side object detection system may also select or adjust the image processing to select/adjust the areas of interest, such as in response to a steering angle of the vehicle, such as a turning of the wheels of the vehicle, so that the zone or area is adapted for the turning of the subject vehicle. The imaging system of the present invention thus provides enhanced processing of captured images to provide the desired function of the imaging system or associated control or control system or alert system.
These and other objects, advantages, purposes and features of the present invention will become apparent upon review of the following specification in conjunction with the drawings.
Referring now to the drawings and the illustrative embodiments depicted therein, an object detection system or imaging system, such as a lane change assist or aid system 10, is positioned at a vehicle 12 (such as at an exterior rearview mirror 12a of a vehicle) and is operable to capture an image of a scene occurring sidewardly and rearwardly at or along one or both sides of vehicle 12 (
Optionally, the imaging system and object detection system of the present invention may utilize aspects of the imaging systems or detection systems of the types described in U.S. Pat. Nos. 5,929,786 and/or 5,786,772, and/or U.S. patent application Ser. No. 10/427,051, filed Apr. 30, 2003, now U.S. Pat. No. 7,038,577; and/or Ser. No. 11/239,980, filed Sep. 30, 2005, now U.S. Pat. No. 7,881,496, and/or U.S. provisional applications, Ser. No. 60/628,709, filed Nov. 17, 2004; Ser. No. 60/614,644, filed Sep. 30, 2004; and/or Ser. No. 60/618,686, filed Oct. 14, 2004, or of the reverse or backup aid systems, such as rearwardly directed vehicle vision systems utilizing principles disclosed in U.S. Pat. Nos. 5,550,677; 5,760,962; 5,670,935; 6,201,642; 6,396,397; 6,498,620; 6,717,610 and/or 6,757,109, and/or U.S. patent application Ser. No. 10/418,486, filed Apr. 18, 2003, now U.S. Pat. No. 7,005,974, or of automatic headlamp controls, such as the types described in U.S. Pat. Nos. 5,796,094 and/or 5,715,093; and/or U.S. patent application Ser. No. 11/105,757, filed Apr. 14, 2005, now U.S. Pat. No. 7,526,103; and U.S. provisional applications, Ser. No. 60/607,963, filed Sep. 8, 2004; and Ser. No. 60/562,480, filed Apr. 15, 2004, or of rain sensors, such as the types described in U.S. Pat. Nos. 6,250,148 and 6,341,523, or of other imaging systems, such as the types described in U.S. Pat. Nos. 6,353,392 and 6,313,454, which may utilize various imaging sensors or imaging array sensors or cameras or the like, such as a CMOS imaging array sensor, a CCD sensor or other sensors or the like, such as the types disclosed in commonly assigned, U.S. Pat. Nos. 5,550,677; 5,760,962; 6,097,023 and 5,796,094, and U.S. patent application Ser. No. 09/441,341, filed Nov. 16, 1999, now U.S. Pat. No. 7,339,149, and/or PCT Application No. PCT/US2003/036177 filed Nov. 14, 2003, published Jun. 3, 2004 as PCT Publication No. WO 2004/047421, with all of the above referenced U.S. patents, patent applications and provisional applications and PCT applications being commonly assigned and being hereby incorporated herein by reference.
As disclosed in U.S. Pat. No. 5,670,935 (incorporated by reference above), an imaging system of the present invention comprises a vehicle vision system including at least two side image capture devices positioned, respectively, on opposite sides of an equipped vehicle and a center image capture device positioned on the lateral centerline of the vehicle. The vehicle vision system additionally includes an image processor for receiving data signals from image capture devices and synthesizing, from the data signals, a composite image which is displayed on a display.
The images captured by image capture devices are juxtaposed on the display by the image processor in a manner which approximates the view from a single virtual image capture device. The vehicle vision system provides a substantially seamless panoramic view without redundant images of objects. The displayed image provides a sense of perspective, which enhances the ability of the driver to judge location and speed of adjacent trailing vehicles.
A left overlap zone and a right overlap zone exist where the horizontal fields of view of the side image capture devices intersect the field of view of the center image capture device. Each overlap zone defines an area within which an object will be captured both by the center image capture device and one of the side image capture devices.
An image display device displays a composite image made up of a left image portion, a right image portion, and a center image portion. Each image portion is reversed from the image as captured by the respective image capture device utilizing conventional techniques. These techniques include reading the image in reverse with the image capture device, writing the image in reverse to the display device, or reversing the image in the image processor. The left image portion is joined with the central image portion at a boundary. The central image portion is joined with the right image portion at a boundary. The image portions at the boundaries are continuous whereby the composite image is a seamless panoramic view. The central image portion is narrower than either the left image portion or the right image portion. The composite image provides a clear image, which avoids confusion.
The display is preferably positioned within the driver's physiological field of view without obstructing the view through the windshield. It is known that the driver's field of view, with the head and eyes fixed forward, extends further in a downward direction than in an upward direction. The display could be located above the vertical view through the windshield wherein the display may be observed at the upward portion of the driver's field of view. However, the position for the display is preferred wherein the display is within the lower portion of the driver's field of view.
The display is a flat panel display, such as a back-lit liquid crystal display, a plasma display, a field emission display, or a cathode ray tube.
Each image-capturing device is controlled by appropriate supporting electronics located in the vicinity of the imaging array such that, when operating power is supplied, either an analog or a digital data stream is generated on an output signal line supplied to the image processor. The support electronics may be provided partially on the image chip and partially on associated electronic devices. For each exposure period, a value indicative of the quantity of light incident on each pixel of the imaging array during the exposure period is sequentially outputted in a predetermined sequence, typically row-by-row. The sequence may conform to video signal standards which support a direct view such that, when a scene is viewed by an image-capturing device, the image presented on a display represents directly the scene viewed by the image-capturing devices.
Data transmission between image capture devices and the image processor and/or between the image processor and the display may be by electrically conductive leads or fiber-optic cable.
The data streams from image-capturing devices are combined in the image processor and directly mapped to the pixel array of display. The image captured by the side image capture device on the right side of the vehicle is presented on a right image portion and the image from the side image capture device on the left side of the vehicle is displayed on a left image portion. The image from the center image capture device is displayed on a central image portion. The three image portions are presented in horizontal alignment and adjacent to each other. However, the composite image may be positioned at any desired vertical position in the display.
The image sensor may be located at the vehicle so as to have a sideward field of view, such as at an exterior rearview mirror of the vehicle, such as generally or at least partially within an exterior rearview mirror of the vehicle. For example, an image sensor may be located within an exterior rearview mirror assembly of the vehicle and may have a generally rearwardly and sidewardly field of view through a transflective reflective element of the exterior rearview mirror assembly. In such an application, the image sensor may be incorporated in or associated with a side object detection system that detects objects at a side or blind spot area of the controlled or subject vehicle. Optionally, the image sensor may have a generally forward field of view to capture images of a scene occurring forwardly of the vehicle. The image sensor may be located within the vehicle cabin and rearward of the windshield so as to have a field of view forwardly and through the windshield of the vehicle, preferably at a location that is cleaned by the windshield wipers of the vehicle, such as at an interior rearview mirror assembly of the vehicle or at an accessory module or windshield electronics module or the like. In such an application, the image sensor may be incorporated in or associated with a lane departure warning system that detects a departure of the controlled or subject vehicle from a lane as the vehicle travels along a road.
Camera Calibration:
In order to verify that the camera or imaging sensor is mounted at the vehicle (such as at an exterior portion of the vehicle) within a desired tolerance limit so as to provide the desired field of view, the camera may detect the side of the vehicle (shown at 30 in
For example, the algorithm may function to preprocess the captured image by a histogram equalization to improve the image contrast. The algorithm may then process the captured images via an edge detection in the area of interest to extract the expected edge of the vehicle (shown at 34 in
After the image data or image processing is adjusted to account for any misalignment of the camera at the vehicle, the camera may capture images of the scene occurring exteriorly of the vehicle and at that side of the vehicle, and the control may process the images to detect objects or lane markers or the like at the side of the vehicle and/or rearward of the vehicle, and may utilize aspects described in U.S. patent application Ser. No. 10/427,051, filed Apr. 30, 2003, now U.S. Pat. No. 7,038,577, which is hereby incorporated herein by reference.
Adjustment of Zone when Vehicle Turning:
Optionally, the control may perform a curve processing or lane divider curve fitting function to select or adjust the reduced image set or zone of interest based on a steering angle of the vehicle (the angle at which the vehicle is steered to either side relative to the longitudinal axis of the vehicle), such as when the front wheels of the subject vehicle are turned. The curve processing function may adjust the zone of interest based on the degree of turning of the steering wheel (or other characteristic indicative of the steering angle of the vehicle) so that areas or regions outside of a revised curve may not be processed or considered as much as the areas or regions within the revised curve. Based on the history of host vehicle information (such as vehicle speed and wheel angle), the control may modify the slope of the adjacent lane divider (at the left side of user specified area of interest) at different positions. As shown in
The following vehicle information history table (VIHT) with the history of multiple (N) frames or captured images may be maintained for fitting of a curve. The table may be updated when the vehicle information is available for each frame.
During operation, the control may perform the following computations:
-
- 1. Compute Reference Angle (θREF) using polygon vertices (1 & 5).
- 2. Compute Reference Point (XREF, YREF) using Reference Angle (θREF) and Mirror Wheel Separation Distance (D).
- 3. For each frame update (add in the circular buffer) following VIHT entries:
- a. Frame Index or Time Stamp (Tn);
- b. Host Vehicle Speed (Vn); and
- c. Host Vehicle Wheel Angle (θn) (note that this angle may be considered without doing any perspective correction, but may require appropriate correction to be added based on height and angle of inclination of the camera).
- 4. Compute Cumulative Wheel Angle (θCn):
-
- 5. Compute curve points (Xn, Yn) (such as in cm):
- The n=N curve point is a reference point and for all other points the following equations may be used to compute the curve points:
YN-1=F1(TN-1,VN-1,YN); and
XN-1=F2(θC(N-1),YN-1,XN,YN). - Preferably, the functions F1 and F2 are relatively simple trigonometric functions.
- The n=N curve point is a reference point and for all other points the following equations may be used to compute the curve points:
- 6. Replace the curve point (X0, Y0) with the intersection point of the curve and the horizon, and preferably do not allow the curve to cross the horizon.
- 7. For display purposes, compute the curve points (XPn, YPn) in pixels using the existing cm—pixel relations (CMDistances and PixelSomethingThreshold)
- 5. Compute curve points (Xn, Yn) (such as in cm):
In a preferred embodiment, the steps 1 and 2 above may be a one time computation in the initialization part of the algorithm, while the steps 3-6 are preferably executed during the run-time of the system, and the step 7 preferably is executed in the debug mode for the testing.
Vertical Edge Detection:
Optionally, the object detection system may be operable to detect vertical edges of objects in the field of view of the camera. Such an approach may be useful in detecting target vehicles when the front of the target vehicle is not visible, such as when the target vehicle is alongside the subject vehicle.
The vertical edge detection algorithm may be performed first at a low level Processing. During such low level processing, the vertical edge detection may be applied to an input image, and a noise filtering algorithm may be applied to the edge image. The lateral X histogram may be calculated and analyzed, and any non-significant edge counts may be removed from the histogram. The histogram may be normalized and analyzed to identify probable targets, a different ID may be given for a vertical edge detected target for use during high level processing.
During the high level processing, a map of target information for vertical targets may be generated, such as in a similar manner as for horizontal edge detected targets (such as described in U.S. patent application Ser. No. 10/427,051, filed Apr. 30, 2003, now U.S. Pat. No. 7,038,577, which is hereby incorporated herein by reference, and such as described below). The targets may be filtered out based on various criteria (such as relative speed, number of frames present and/or the like), and a warning may be raised or generated if the object is detected at the side of the vehicle.
Such vertical edge detection functions may be desirable, since a target vehicle's trunk may generate a significant vertical edge. Also, the vertical edges may substantially always be present in both the daytime and nighttime hot zones. Furthermore, tree shadows, lane markings and other structures may be filtered out using such vertical edge detection.
Qualification During Daytime:
Side Object Detection works based on the edges detected. Horizontal edges are used to detect and track vehicle. The detected edges are qualified based on a robust high level vision algorithm, such as the algorithms described in U.S. patent application Ser. No. 10/427,051, filed Apr. 30, 2003, now U.S. Pat. No. 7,038,577, which is hereby incorporated herein by reference. Vertical edges are being used to detect vehicles close to the camera/passing vehicles, as discussed above.
However, the vehicle identification may be based on the shadow created by the vehicle (for day time operation). A detected shadow could be created by a target vehicle entering into the blind spot or static objects like guard rails or structures on the road side or by the self shadow (of the subject vehicle) or of vehicles passing more than one lane away. It is difficult to identify whether or not the shadow is by a target vehicle entering into the blind spot. Thus, a positive identification or qualification of the edges can help in reducing false alarms.
When a shadow is detected, it is either a shadow created by a vehicle entering into the blind spot, or a shadow created by something else and not the target vehicle (a vehicle entering the blind spot). Referring to
In the case where the shadow is created by something other than the target vehicle, the following characteristics may be assumed:
-
- 1. Below the edge (for horizontal edges) the road may have full illumination by daylight, and above the horizontal edge there will be road where daylight is partially occluded by some objects (so lesser illumination); or
- 2. Below the edge the road may be darker because of partial occlusion by some object causing the shadow, and above the edge the road may have full daylight illumination;
- 3. The above two scenarios are similar for the areas to the left and right of a vertical shadow.
Therefore, the following characteristics can also be assumed:
1. Above and below the edges there is road;
2. To the left and right of a vertical edge there is road;
3. One side of the edge is not road but is a vehicle.
The problem is that it has heretofore been difficult to distinguish between the shadow and the vehicle. The proposed methodology is intended to increase the confidence in decision. For example, the situations in
In order to qualify a detected edge, the system of the present invention may perform the following processing steps:
1. Histogram Analysis:
-
- display the edge pixels within the hot zone;
- determine the start and the endpoint of an edge;
- create processing windows on either side of the edge; and
- perform wide line integration.
For Example, and as shown in
2. Extracting unique features across the edge (mean and range):
-
- display the edge pixels within the hot zone;
- determine the start and the endpoint of an edge;
- estimate unique features to qualify an edge such as mean and range; and
- analyze variations in mean and range on either side of the edge.
Comparison of the mean and range of the processing windows across a detected edge is expected to give insight in qualifying an edge.
3. Gradient analysis across the detected horizontal edge:
-
- display the edge pixels within the hot zone;
- determine the start and the endpoint of an edge;
- gradient calculations are done on the processing windows using local window operators, such as Sobel operators of the mask shown in
FIG. 13 ; and - The gradient magnitude gives the amount of the difference between pixels in the neighborhood (the strength of the edge). The gradient orientation gives the direction of the greatest change, which presumably is the direction across the edge (the edge normal). Comparison of the gradient across the edge is expected to provide insight in qualifying an edge.
Host Shadow Removal:
In side object detection, the host shadow of the vehicle may be detected as a target vehicle if the host shadow is extended in the hot zone (or zone or area of interest alongside the subject or host vehicle). Often, the host shadow may fall on the adjacent lane in the morning or evening time. The host shadow consists of a straight horizontal edge and an edge line with some slope, such as shown in
The steps involved in processing the image to remove or ignore the host shadow include:
-
- obtain the target list at the output of processframe( );
- for each target, get the row value (y_Pixel);
- trace for the horizontal edge line around this row value;
- check whether this line is continuous, starting from the subject vehicle body (approximately) and up to and after the lower border of the hot zone;
- check whether this horizontal edge is extended in the hot zone and has a significant pixel count in the hot zone (check against the pixelsomething threshold for that particular line);
- start tracing for the sloped edge line at the end of this horizontal edge;
- if the sloped line is present for some columns it means that there is a host shadow; and
- if there is a host shadow, remove the corresponding target from the target list.
Note that this process may be applied before calculating the target list, so that the host shadow edge pixel count can be subtracted from the line counts. In such an application, all the lines may be scanned until the detection of the host shadow. Vertical edge calculations may not be needed for this process, since, often, if the horizontal edge line of the host shadow is present in the hot zone then the host shadow is getting detected.
Bicycle Detection:
The side object detection system may be operable to detect other objects, such as a bicycle in the zone of interest. For a bicycle, there are small edges compared to a target vehicle, since the bicycle tire is small. In order to detect and track such small edges, we need to tune some of the parameters. For example, some or all of the following listed parameters may be changed to enhance the ability of the system to detect a bicycle:
-
- SOMETHING_SIZE_CM: some size of the object in cms for reference (Calibration.h);
- gCalibration.SMOOTHING_ALG_THRESHOLD: threshold applied on the peak normal force of the vehicle to see if the detected vehicle was significant (FindEdgePeaks_A( ));
- gCalibration.XFILTER_FACTOR: with the pixelsomething threshold of a line, it decides the minimum number of horizontal pixels required for a valid object/target (XFilterNew( ));
- gCalibration.HOT_ZONE_COUNT_THRESHOLD_DIVISOR: divisor factor of pixel something threshold of a line at the time of detection of valid objects (ComputeLineCountsR( ));
- gCalibration.X_MUST_WARN: the maximum distance for a target to create a warning (when a target is located inside this position (Target X<X Must Warn), a warning for that target may be displayed);
- gCalibration.VD_HYSTERESIS_ON: threshold (normal force) at which the algorithm determines a starting point of a target (FindEdgePeaks_A( ));
- gCalibration.VD_HYSTERESIS_OFF: threshold at which the algorithm determines the falling off point of a target (FindEdgePeaks_A( ));
- gCalibration.LINEERROR: this is the difference between the projected distance and actual distance of a particular target (i.e. line mapping error).
With the parameter tuning, the system may detect many false edges as well. In order to reduce such false edge detections, the vertical edge detection may be used as an additional feature.
Vertical edge detection:
-
- detect the vertical edges for the frame in the area of interest region;
- the vertical edge image is scanned for the connected pixels along the columns;
- the maximum connected pixels count is compared against the threshold;
- based on the comparison result, the decision is made as to whether the target is present or absent.
The vertical edge image may be scanned in the following regions:
-
- Lower limit: the lower limit of the area of interest;
- Upper limit: the upper boundary of the nighttime hot zone (FarCenterIndices_N).
This is to reduce or substantially avoid a false vertical edge detection of objects outside the adjacent lane. The vertical edges may correspond to the wheels. When the target vehicle is far away, the vertical edge count will be small, and when the target vehicle is closer, the vertical edge count will be greater. A threshold count may be selected to provide the desired accuracy of the system.
The bicycle detection algorithm may be applied during the daytime because the bicycle headlight can be detected with the existing parameters (to be verified), and because little or no vertical edges may be detected during the night time (the detected vertical edges are mainly because of the wheels). Because the bicycle speed is typically low compared to the vehicle speed, the bicycle detection algorithm may be triggered only when the host vehicle speed goes below some threshold speed (such as at or below approximately 20 kph or thereabouts, for example). A flow chart of the bicycle detection process is shown in
Without the vertical edge detection, some situations may result in false detections. For example, speed breakers beside the road (the system may not consider these as stationary objects because they continuously have edges one after the other which may map with the previous ones, so that some frames in this region are detected as targets), bisectors (the shadow of the bisector may create edges at the upper side of the image, in this region small pixel counts results in edge detection and may result in a detection of a target), roadside dark areas with small white patches. With vertical edge detection as an additional feature, the above false detections may be improved.
Also, it is envisioned that a bicycle rider may not always move straight like a vehicle in the lane. If the front wheel swings, then the projected target may not match with the current detected position. In order to account for this, the line error parameter may be adjusted to some extent. Also, it may be difficult to detect a bicycle if there is a shadow on the road and the bicycle is on the shadowed part of the road.
Target Mapping Tolerance:
Optionally, the object detection system and control may provide a near field detection or target mapping tolerance—line error function. Such a detection may utilize use inputs or calibration parameters. For example, a line error image may provide target-mapping tolerances in the image space due to car vibrations (such as about 3 to 6 pixels), or errors in the image processing (e.g. about 1 pixel for an edge filter), or other inputs. Also, a minimum line error (physical) input may provide target mapping tolerances in the physical space due to small variations in the target vehicle speed (this calibration parameter may be in terms of cms). Likewise, a target speed tolerance factor (such as between 0-100) may provide target mapping tolerances in the physical space, as expected to change with the speed of a target. This factor may determine the size of a tolerance band.
During operation of the object detection system, the control may compute the tolerance (the line error pixels) to be used for the target mapping, which may change with the distance from the camera (i.e., the tolerance may be different for the different lines in the image).
The following provides computations/functions that the control may perform to determine the tolerances and the like.
For example, the computation for the Nth line:
LineErrorPixels=MAX(LineErrorImage,LineErrorPhysicalInPixels_N).
LineErrorPhysicalInPixels_N=CMtoPIXEL(LineErrorPhysical,N).
CMtoPIXEL (LineErrorPhysical, N) is a function that will convert line error in cm to pixels for a specified line.
LineErrorPhysical=MAX(MinLineErrorPhysical,LineErrorPhysicalWindow).
LineErrorPhysicalWindow=(TargetSpeedToleranceFactor*ExpectedDistanceCoveredByTarget)/100.
ExpectedDistanceCoveredByTarget=TargetRelativeSpeed*FrameRate.
Day/Night Switch Algorithm:
Optionally, the side object detection system may switch between a daytime sensitivity and a nighttime sensitivity. For example, the algorithm may include the following steps:
1. Compute image histogram (H);
2. compute accumulated image histogram (Ha) using last N (such as, for example, 30) frames;
3. divide Ha into the following four regions:
-
- (1) dark (0<Gray Level<=Td);
- (2) night (Td<Gray Level<=Tdn);
- (3) day (Tdn<Gray Level<Tb); and
- (4) bright (Tb<=Gray Level<=255),
- using following three thresholds:
- (1) Td (0): dark threshold;
- (2) Tdn (80): day-night threshold (which may be selected in such a way that it separates day (with/without shadow) from the night (with/without headlights); this may be a fixed threshold computed using arithmetic mean or a geometric mean and auto-threshold); and
- (3) Tb (255): bright threshold that is used to separate headlight and overexposed (during day) pixels;
4. count number of pixels into above four regions:
-
- (1) dark region count (Cd);
- (2) night region count (Cn);
- (3) day region count (Cy); and
- (4) bright region count (Cb),
- and compute:
- (1) difference in Cy and Cn in % of total pixels (Ddn) [where Ddn=(ABS(Cy−Cn)*100)/ImagePixelCount]; and
- (2) extreme peak count (Cd+Cb) in % of total pixels (P) [where P=((Cd+Cb)*100)/ImagePixelCount]; and
5. apply the following rules to make a decision:
For example, T1=70 percent and T2=2 percent. For rule 4 and 5, additional features may be desired, since the available features may not in all cases be sufficient to differentiate between day and night. If no determination or differentiation is made, the previous state may be retained. The processing time for each frame may be around 1 ms or thereabouts, such as about 0.7 ms.
Headlight Detection:
Optionally, a headlight detection module may be used to improve the nighttime target detection results. Such a module may be integrated with the side object detection algorithm. The headlight detection module or algorithm may use optimized blob analysis code in area of interest, and may identify clusters in an image during nighttime detection mode. The headlight detection module or algorithm may separate out headlights, and saturated and unqualified clusters of lights, and may be able to distinguish between headlights and reflections caused by headlights. The headlight detection module or algorithm may minimize false detections caused because of reflections on the road, or non-headlight light sources. The headlight detection module may detect the position of the headlight, and thus may provide enhanced positional accuracy while target detection. The headlight detection module may also be useful in dusk and dawn condition.
It is envisioned that, by analysis of the saturated clusters, the camera gain and exposure can be controlled in close loop, and thus the day/night switch detection can be improved. It is further envisioned that, for more accuracy, the system may detect both headlights to assist in making the decision of target detection (such as for detecting cars and/or trucks). Also, most or substantially all of the saturated and not qualified clusters may be examined for possible merging of two headlights, such as when the vehicle is far away from the camera on the subject or host vehicle.
Referring now to
The table below indicates the effect on the timing of the detection/identification of side objects, as shown with and without the headlight detection function:
Optionally, the side object detection system and/or the imaging sensor or camera may be associated with other imaging systems or controls, such as imaging systems having an image sensor (such as a video camera, such as a CMOS imaging array sensor, a CCD sensor or the like, such as the types of imaging sensors or cameras disclosed in commonly assigned, U.S. Pat. Nos. 5,550,677; 6,097,023 and 5,796,094, and/or PCT Application No. PCT/US2003/036177, filed Nov. 14, 2003 and published Jun. 3, 2004 as International Publication No. WO 2004/047421, which are hereby incorporated herein by reference), such as a forward facing video image sensor or system, which may include or may be associated with an intelligent rain sensor (such as the types disclosed in commonly assigned U.S. Pat. Nos. 6,320,176; 6,353,392 and 6,313,454, which are hereby incorporated herein by reference), an image or vision system (including an imaging sensor, such as a video camera, such as a CMOS imaging array sensor, a CCD sensor or the like, such as the types disclosed in commonly assigned, U.S. Pat. Nos. 5,550,677; 6,097,023 and 5,796,094, and U.S. patent application Ser. No. 10/422,378, filed Apr. 24, 2003, now U.S. Pat. No. 6,946,978, which are hereby incorporated herein by reference), such as an imaging or object detection system or back up aid of the types described in U.S. Pat. Nos. 6,757,109; 6,717,610; 6,590,719; 6,201,642; 5,929,786 and/or 5,786,772, and/or U.S. patent application Ser. No. 10/427,051, filed Apr. 30, 2003, now U.S. Pat. No. 7,038,577; and/or Ser. No. 11/239,980, filed Sep. 30, 2005, now U.S. Pat. No. 7,881,496, and/or U.S. provisional applications, Ser. No. 60/628,709, filed Nov. 17, 2004; Ser. No. 60/614,644, filed Sep. 30, 2004; and/or Ser. No. 60/618,686, filed Oct. 14, 2004, which are hereby incorporated herein by reference, or an intelligent headlamp controller (such as the types described in U.S. Pat. Nos. 5,796,094; 5,715,093 and/or 6,824,281, and/or in U.S. patent application Ser. No. 10/958,087, filed Oct. 4, 2004, now U.S. Pat. No. 7,188,963; and/or Ser. No. 11/105,757, filed Apr. 14, 2005, now U.S. Pat. No. 7,526,103, and/or U.S. provisional applications, Ser. No. 60/607,963, filed Sep. 8, 2004; and Ser. No. 60/562,480, filed Apr. 15, 2004, which are hereby incorporated herein by reference), or an intelligent lane departure warning system (such as the types described in U.S. patent application Ser. No. 10/209,173, filed Jul. 31, 2001, now U.S. Pat. No. 6,882,287; and/or Ser. No. 10/427,051, filed Apr. 30, 2003, now U.S. Pat. No. 7,038,577, which are hereby incorporated herein by reference), and/or the like.
Optionally, the side object detection system of the present invention may include one or more displays, such as a text display, an icon display, a display on demand (DOD) type display (such as may be implemented with a transflective reflective element, such as described in U.S. Pat. Nos. 5,668,663; 5,724,187 and 6,690,268, the entire disclosures of which are hereby incorporated by reference herein), such as a video or touch screen interface display, or a video display screen, such as the types described in PCT Application No. PCT/US03/40611, filed Dec. 19, 2003 and published on Jul. 15, 2004 as International Publication No. WO 2004/058540, and/or U.S. patent application Ser. No. 10/538,724, filed Jun. 13, 2005 and published Mar. 9, 2006 as U.S. Publication No. US-2006-0050018; and Ser. No. Ser. No. 11/284,543, filed Nov. 22, 2005, now U.S. Pat. No. 7,370,983; and/or U.S. provisional applications, Ser. No. 60/630,061, filed Nov. 22, 2004; and Ser. No. 60/667,048, filed Mar. 31, 2005, which are hereby incorporated herein by reference, or the like.
Therefore, the present invention provides a side object detection system that is operable to process captured image data of a scene occurring exteriorly and along one or both sides of the vehicle to determine if a target vehicle or object of interest is located at or in the lane adjacent to the subject or host vehicle. The side object detection system of the present invention may process zones or areas of interest in the captured images and may adjust processing to accommodate any misalignment of the camera that may occur during installation of the camera at the side of the vehicle. The side object detection system may also adjust the processing to adjust the areas of interest, such as in response to a turning of the wheels of the vehicle so that the zone or area is adapted for the turning of the subject vehicle. The side object detection system may detect horizontal edges and vertical edges of objects in the exterior scene and may identify shadows and discern the shadows in the adjacent lane (which may be shadows cast by objects outside of the adjacent lane) from objects or target vehicles that may be present in the adjacent lane. The side object detection system of the present invention may also discern or distinguish between daytime and nighttime conditions and may adjust the image processing accordingly. The side object detection system may also function to detect headlights of a target vehicle in the adjacent lane and to discern or distinguish the headlights of the target vehicle from other light sources, such as reflections or other non-qualified light sources. The side object detection system of the present invention thus provides enhanced image processing and may utilize aspects of the side object detection system described in U.S. patent application Ser. No. 10/209,173, filed Jul. 31, 2001, now U.S. Pat. No. 6,882,287; and/or Ser. No. 10/427,051, filed Apr. 30, 2003, now U.S. Pat. No. 7,038,577, which are hereby incorporated herein by reference.
Changes and modifications in the specifically described embodiments can be carried out without departing from the principles of the present invention, which is intended to be limited only by the scope of the appended claims as interpreted according to the principles of patent law.
Claims
1. A vehicular vision system, said vehicular vision system comprising:
- a first camera comprising an imaging sensor having an array of photosensing pixels;
- a second camera comprising an imaging sensor having an array of photosensing pixels;
- a rear backup camera comprising an imaging sensor having an array of photosensing pixels;
- a control comprising a processor for processing image data captured by said first camera, by said second camera and by said rear backup camera;
- wherein said first camera is disposed at a left side of a vehicle equipped with said vehicular vision system;
- wherein said first camera has a field of view at least rearward of the equipped vehicle and sideward of the left side of the equipped vehicle;
- wherein said second camera is disposed at a right side of the equipped vehicle;
- wherein said second camera has a field of view at least rearward of the equipped vehicle and sideward of the right side of the equipped vehicle;
- wherein, when said first camera is disposed at the left side of the equipped vehicle, a portion of the left body side of the equipped vehicle is present in the field of view of said first camera;
- wherein, when said second camera is disposed at the right side of the equipped vehicle, a portion of the right body side of the equipped vehicle is present in the field of view of said second camera;
- wherein said first camera is disposed at a left-side exterior mirror assembly of the equipped vehicle;
- wherein said second camera is disposed at a right-side exterior mirror assembly of the equipped vehicle;
- wherein said rear backup camera is disposed at a rear portion of the equipped vehicle centrally between where said first camera is disposed at the vehicle and where said second camera is disposed at the vehicle;
- wherein said rear backup camera has a field of view at least rearward of the equipped vehicle;
- wherein the field of view of said first camera overlaps with the field of view of said rear backup camera, and wherein the field of view of said second camera overlaps with the field of view of said rear backup camera;
- wherein image data captured by said first camera, by said second camera and by said rear backup camera is processed by said processor of said control and, at least in part responsive to processing by said processor of image data captured by said first camera, by said second camera and by said rear backup camera, said control outputs a composite image for display at a display disposed in an interior cabin of the equipped vehicle at a location viewable by a driver of the equipped vehicle when the driver is operating the equipped vehicle;
- wherein the displayed composite image provides a view as would be provided from a single virtual image capture device;
- wherein said control, responsive at least in part to processing by said processor of image data captured by said first camera, determines that said first camera is misaligned due to a shift in at least one of yaw, pitch and roll alignment of said first camera disposed at the left side of the equipped vehicle;
- wherein said control, responsive to the determination of misalignment of said first camera, at least partially compensates for the misalignment of said first camera by at least one of (i) adjusting processing by said processor of image data captured by said first camera and (ii) adjusting image data captured by said first camera;
- wherein said control, responsive at least in part to processing by said processor of image data captured by said second camera, determines that said second camera is misaligned due to a shift in at least one of yaw, pitch and roll alignment of said second camera disposed at the right side of the equipped vehicle;
- wherein said control, responsive to the determination of misalignment of said second camera, at least partially compensates for the misalignment of said second camera by at least one of (i) adjusting processing by said processor of image data captured by said second camera and (ii) adjusting image data captured by said second camera;
- wherein said control, responsive at least in part to processing by said processor of image data captured by said rear backup camera, determines that said rear backup camera is misaligned due to a shift in at least one of yaw, pitch and roll alignment of said rear backup camera disposed at the rear portion of the equipped vehicle; and
- wherein said control, responsive to the determination of misalignment of said rear backup camera, at least partially compensates for the misalignment of said rear backup camera by at least one of (i) adjusting processing by said processor of image data captured by said rear backup camera and (ii) adjusting image data captured by said rear backup camera.
2. The vehicular vision system of claim 1, wherein the displayed composite image provides a seamless panoramic view.
3. The vehicular vision system of claim 2, wherein said imaging array of said first camera comprises a CMOS imaging array sensor.
4. The vehicular vision system of claim 3, wherein said imaging array of said second camera comprises a CMOS imaging array sensor.
5. The vehicular vision system of claim 4, wherein said imaging array of said rear backup camera comprises a CMOS imaging array sensor.
6. The vehicular vision system of claim 1, wherein said first camera is disposed at least partially within the left-side exterior mirror assembly of the equipped vehicle.
7. The vehicular vision system of claim 6, wherein said second camera is disposed at least partially within the right-side exterior mirror assembly of the equipped vehicle.
8. The vehicular vision system of claim 7, wherein the portion of the left body side of the equipped vehicle present in the field of view of said first camera comprises a door handle.
9. The vehicular vision system of claim 8, wherein the portion of the right body side of the equipped vehicle present in the field of view of said second camera comprises a door handle.
10. The vehicular vision system of claim 1, wherein said control, responsive at least in part to processing by said processor of image data captured by said first camera, calculates a degree of misalignment of said first camera.
11. The vehicular vision system of claim 10, wherein said control, responsive at least in part to processing by said processor of image data captured by said second camera, calculates a degree of misalignment of said second camera.
12. The vehicular vision system of claim 11, wherein said control, responsive at least in part to processing by said processor of image data captured by said rear backup camera, calculates a degree of misalignment of said rear backup camera.
13. The vehicular vision system of claim 1, wherein processing by said processor of image data captured by said first camera at least one of (i) adjusts image data, (ii) shifts image data and (iii) rotates image data.
14. The vehicular vision system of claim 13, wherein processing by said processor of image data captured by said second camera at least one of (i) adjusts image data, (ii) shifts image data and (iii) rotates image data.
15. The vehicular vision system of claim 14, wherein processing by said processor of image data captured by said rear backup camera at least one of (i) adjusts image data, (ii) shifts image data and (iii) rotates image data.
16. The vehicular vision system of claim 1, wherein, in accordance with the ambient light level at the equipped vehicle, said control operates with a daytime sensitivity or a nighttime sensitivity.
17. The vehicular vision system of claim 1, wherein processing by said processor of image data comprises use of an algorithmically executed filter.
18. The vehicular vision system of claim 1, wherein said display comprises a flat panel display and wherein said flat panel display comprises a back-lit liquid crystal display.
19. A vehicular vision system, said vehicular vision system comprising:
- a first camera comprising an imaging sensor having an array of photosensing pixels;
- a second camera comprising an imaging sensor having an array of photosensing pixels;
- a rear backup camera comprising an imaging sensor having an array of photosensing pixels;
- a control comprising a processor for processing image data captured by said first camera, by said second camera and by said rear backup camera;
- wherein said first camera is disposed at a left side of a vehicle equipped with said vehicular vision system;
- wherein said first camera has a field of view at least rearward of the equipped vehicle and sideward field of the left side of the equipped vehicle;
- wherein said second camera is disposed at a right side of the equipped vehicle;
- wherein said second camera has a field of view at least rearward of the equipped vehicle and sideward of the right side of the equipped vehicle;
- wherein, when said first camera is disposed at the left side of the equipped vehicle, a portion of the left body side of the equipped vehicle is present in the field of view of said first camera;
- wherein, when said second camera is disposed at the right side of the equipped vehicle, a portion of the right body side of the equipped vehicle is present in the field of view of said second camera;
- wherein said first camera is disposed at a left-side exterior mirror assembly of the equipped vehicle;
- wherein said second camera is disposed at a right-side exterior mirror assembly of the equipped vehicle;
- wherein said rear backup camera is disposed at a rear portion of the equipped vehicle;
- wherein said rear backup camera has a field of view at least rearward of the equipped vehicle;
- wherein the field of view of said first camera overlaps with the field of view of said rear backup camera, and wherein the field of view of said second camera overlaps with the field of view of said rear backup camera;
- wherein image data captured by said first camera, by said second camera and by said rear backup camera is processed by said processor of said control and, at least in part responsive to processing by said processor of image data captured by said first camera, by said second camera and by said rear backup camera, said control outputs a composite image for display at a display disposed in an interior cabin of the equipped vehicle at a location viewable by a driver of the equipped vehicle when the driver is operating the equipped vehicle;
- wherein the displayed composite image provides a view as would be provided from a single virtual image capture device;
- wherein said control, responsive at least in part to processing by said processor of image data captured by said first camera, determines that said first camera is misaligned due to a shift in at least one of yaw, pitch and roll alignment of said first camera disposed at the left side of the equipped vehicle;
- wherein said control, responsive to the determination of misalignment of said first camera, at least partially compensates for the misalignment of said first camera by adjusting processing by said processor of image data captured by said first camera;
- wherein said control, responsive at least in part to processing by said processor of image data captured by said second camera, determines that said second camera is misaligned due to a shift in at least one of yaw, pitch and roll alignment of said second camera disposed at the right side of the equipped vehicle;
- wherein said control, responsive to the determination of misalignment of said second camera, at least partially compensates for the misalignment of said second camera by adjusting processing by said processor of image data captured by said second camera; and
- wherein the displayed composite image provides a seamless panoramic view.
20. The vehicular vision system of claim 19, wherein said control, responsive at least in part to processing by said processor of image data captured by said rear backup camera, determines that said rear backup camera is misaligned due to a shift in at least one of yaw, pitch and roll alignment of said rear backup camera disposed at the rear portion of the equipped vehicle, and wherein said control, responsive to the determination of misalignment of said rear backup camera, at least partially compensates for the misalignment of said rear backup camera by adjusting processing by said processor of image data captured by said rear backup camera.
21. The vehicular vision system of claim 20, wherein said imaging array of said first camera comprises a CMOS imaging array sensor, and wherein said imaging array of said second camera comprises a CMOS imaging array sensor, and wherein said imaging array of said rear backup camera comprises a CMOS imaging array sensor.
22. The vehicular vision system of claim 21, wherein said first camera is disposed at least partially within the left-side exterior mirror assembly of the equipped vehicle, and wherein said second camera is disposed at least partially within the right-side exterior mirror assembly of the equipped vehicle.
23. The vehicular vision system of claim 22, wherein the portion of the left body side of the equipped vehicle present in the field of view of said first camera comprises a door handle, and wherein the portion of the right body side of the equipped vehicle present in the field of view of said second camera comprises a door handle.
24. The vehicular vision system of claim 22, wherein said control, responsive at least in part to processing by said processor of image data captured by said first camera, calculates a degree of misalignment of said first camera, and wherein said control, responsive at least in part to processing by said processor of image data captured by said second camera, calculates a degree of misalignment of said second camera, and wherein said control, responsive at least in part to processing by said processor of image data captured by said rear backup camera, calculates a degree of misalignment of said rear backup camera.
25. The vehicular vision system of claim 22, wherein processing by said processor of image data captured by said first camera at least one of (i) adjusts image data, (ii) shifts image data and (iii) rotates image data, and wherein processing by said processor of image data captured by said second camera at least one of (i) adjusts image data, (ii) shifts image data and (iii) rotates image data, and wherein processing by said processor of image data captured by said rear backup camera at least one of (i) adjusts image data, (ii) shifts image data and (iii) rotates image data.
26. The vehicular vision system of claim 22, wherein said display comprises a flat panel display and wherein said flat panel display comprises a back-lit liquid crystal display.
27. The vehicular vision system of claim 19, wherein said rear backup camera is disposed centrally between where said first camera is disposed at the vehicle and where said second camera is disposed at the vehicle.
28. A vehicular vision system, said vehicular vision system comprising:
- a first camera comprising a CMOS imaging sensor having an array of photosensing pixels;
- a second camera comprising a CMOS imaging sensor having an array of photosensing pixels;
- a rear backup camera comprising a CMOS imaging sensor having an array of photosensing pixels;
- a control comprising a processor for processing image data captured by said first camera, by said second camera and by said rear backup camera;
- wherein said first camera is disposed at a left side of a vehicle equipped with said vehicular vision system;
- wherein said first camera has a field of view at least rearward of the equipped vehicle and sideward of the left side of the equipped vehicle;
- wherein said second camera is disposed at a right side of the equipped vehicle;
- wherein said second camera has a field of view at least rearward of the equipped vehicle and sideward of the right side of the equipped vehicle;
- wherein, when said first camera is disposed at the left side of the equipped vehicle, a portion of the left body side of the equipped vehicle is present in the field of view of said first camera;
- wherein, when said second camera is disposed at the right side of the equipped vehicle, a portion of the right body side of the equipped vehicle is present in the field of view of said second camera;
- wherein said first camera is disposed at least partially within a left-side exterior mirror assembly of the equipped vehicle, and wherein said second camera is disposed at least partially within a right-side exterior mirror assembly of the equipped vehicle;
- wherein said rear backup camera is disposed at a rear portion of the equipped vehicle;
- wherein said rear backup camera has a field of view at least rearward of the equipped vehicle;
- wherein the field of view of said first camera overlaps with the field of view of said rear backup camera, and wherein the field of view of said second camera overlaps with the field of view of said rear backup camera;
- wherein image data captured by said first camera, by said second camera and by said rear backup camera is processed by said processor of said control and, at least in part responsive to processing by said processor of image data captured by said first camera, by said second camera and by said rear backup camera, said control outputs a composite image for display at a display disposed in an interior cabin of the equipped vehicle at a location viewable by a driver of the equipped vehicle when the driver is operating the equipped vehicle;
- wherein the displayed composite image provides a view as would be provided from a single virtual image capture device;
- wherein said control, responsive at least in part to processing by said processor of image data captured by said first camera, determines that said first camera is misaligned due to a shift in at least one of yaw, pitch and roll alignment of said first camera disposed at the left side of the equipped vehicle;
- wherein said control, responsive to the determination of misalignment of said first camera, at least partially compensates for the misalignment of said first camera by adjusting image data captured by said first camera;
- wherein said control, responsive at least in part to processing by said processor of image data captured by said second camera, determines that said second camera is misaligned due to a shift in at least one of yaw, pitch and roll alignment of said second camera disposed at the right side of the equipped vehicle; and
- wherein said control, responsive to the determination of misalignment of said second camera, at least partially compensates for the misalignment of said second camera by adjusting image data captured by said second camera.
29. The vehicular vision system of claim 28, wherein the displayed composite image provides a seamless panoramic view.
30. The vehicular vision system of claim 29, wherein said control, responsive at least in part to processing by said processor of image data captured by said rear backup camera, determines that said rear backup camera is misaligned due to a shift in at least one of yaw, pitch and roll alignment of said rear backup camera disposed at the rear portion of the equipped vehicle.
31. The vehicular vision system of claim 30, wherein said control, responsive to the determination of misalignment of said rear backup camera, at least partially compensates for the misalignment of said rear backup camera by adjusting image data captured by said rear backup camera.
32. The vehicular vision system of claim 31, wherein the portion of the left body side of the equipped vehicle present in the field of view of said first camera comprises a door handle, and wherein the portion of the right body side of the equipped vehicle present in the field of view of said second camera comprises a door handle.
33. The vehicular vision system of claim 31, wherein said control, responsive at least in part to processing by said processor of image data captured by said first camera, calculates a degree of misalignment of said first camera, and wherein said control, responsive at least in part to processing by said processor of image data captured by said second camera, calculates a degree of misalignment of said second camera, and wherein said control, responsive at least in part to processing by said processor of image data captured by said rear backup camera, calculates a degree of misalignment of said rear backup camera.
34. The vehicular vision system of claim 31, wherein processing by said processor of image data captured by said first camera at least one of (i) adjusts image data, (ii) shifts image data and (iii) rotates image data, and wherein processing by said processor of image data captured by said second camera at least one of (i) adjusts image data, (ii) shifts image data and (iii) rotates image data, and wherein processing by said processor of image data captured by said rear backup camera at least one of (i) adjusts image data, (ii) shifts image data and (iii) rotates image data.
35. The vehicular vision system of claim 31, wherein said display comprises a flat panel display and wherein said flat panel display comprises a back-lit liquid crystal display.
36. The vehicular vision system of claim 28, wherein said rear backup camera is disposed centrally between where said first camera is disposed at the vehicle and where said second camera is disposed at the vehicle.
2632040 | March 1953 | Rabinow |
2827594 | March 1953 | Rabinow |
3985424 | October 12, 1976 | Steinacher |
4037134 | July 19, 1977 | Loper |
4200361 | April 29, 1980 | Malvano |
4214266 | July 22, 1980 | Myers |
4218698 | August 19, 1980 | Bart et al. |
4236099 | November 25, 1980 | Rosenblum |
4247870 | January 27, 1981 | Gabel et al. |
4249160 | February 3, 1981 | Chilvers |
4254931 | March 10, 1981 | Aikens |
4258979 | March 31, 1981 | Mahin |
4266856 | May 12, 1981 | Wainwright |
4277804 | July 7, 1981 | Robison |
4281898 | August 4, 1981 | Ochiai |
4288814 | September 8, 1981 | Talley et al. |
4355271 | October 19, 1982 | Noack |
4357558 | November 2, 1982 | Massoni et al. |
4381888 | May 3, 1983 | Momiyama |
4420238 | December 13, 1983 | Felix |
4431896 | February 14, 1984 | Lodetti |
4443057 | April 17, 1984 | Bauer |
4460831 | July 17, 1984 | Oettinger et al. |
4481450 | November 6, 1984 | Watanabe et al. |
4491390 | January 1, 1985 | Tong-Shen |
4512637 | April 23, 1985 | Ballmer |
4521804 | June 4, 1985 | Bendell |
4529275 | July 16, 1985 | Ballmer |
4529873 | July 16, 1985 | Ballmer |
4532550 | July 30, 1985 | Bendell et al. |
4546551 | October 15, 1985 | Franks |
4549208 | October 22, 1985 | Kamejima et al. |
4571082 | February 18, 1986 | Downs |
4572619 | February 25, 1986 | Reininger |
4580875 | April 8, 1986 | Bechtel |
4600913 | July 15, 1986 | Caine |
4603946 | August 5, 1986 | Kato |
4614415 | September 30, 1986 | Hyatt |
4620141 | October 28, 1986 | McCumber et al. |
4623222 | November 18, 1986 | Itoh |
4626850 | December 2, 1986 | Chey |
4629941 | December 16, 1986 | Ellis |
4630109 | December 16, 1986 | Barton |
4632509 | December 30, 1986 | Ohmi |
4638287 | January 20, 1987 | Umebayashi et al. |
4645975 | February 24, 1987 | Meitzler et al. |
4647161 | March 3, 1987 | Müller |
4653316 | March 31, 1987 | Fukuhara |
4669825 | June 2, 1987 | Itoh |
4669826 | June 2, 1987 | Itoh |
4671615 | June 9, 1987 | Fukada |
4672457 | June 9, 1987 | Hyatt |
4676601 | June 30, 1987 | Itoh |
4690508 | September 1, 1987 | Jacob |
4692798 | September 8, 1987 | Seko et al. |
4697883 | October 6, 1987 | Suzuki |
4701022 | October 20, 1987 | Jacob |
4713685 | December 15, 1987 | Nishimura et al. |
4717830 | January 5, 1988 | Botts |
4727290 | February 23, 1988 | Smith |
4731669 | March 15, 1988 | Hayashi et al. |
4741603 | May 3, 1988 | Miyagi |
4758883 | July 19, 1988 | Kawahara et al. |
4768135 | August 30, 1988 | Kretschmer et al. |
4772942 | September 20, 1988 | Tuck |
4789904 | December 6, 1988 | Peterson |
4793690 | December 27, 1988 | Gahan |
4817948 | April 4, 1989 | Simonelli |
4820933 | April 11, 1989 | Hong |
4825232 | April 25, 1989 | Howdle |
4838650 | June 13, 1989 | Stewart |
4847772 | July 11, 1989 | Michalopoulos et al. |
4855822 | August 8, 1989 | Narendra et al. |
4859031 | August 22, 1989 | Berman et al. |
4862037 | August 29, 1989 | Farber et al. |
4867561 | September 19, 1989 | Fujii et al. |
4871917 | October 3, 1989 | O'Farrell et al. |
4872051 | October 3, 1989 | Dye |
4881019 | November 14, 1989 | Shiraishi et al. |
4882565 | November 21, 1989 | Gallmeyer |
4886960 | December 12, 1989 | Molyneux |
4891559 | January 2, 1990 | Matsumoto et al. |
4892345 | January 9, 1990 | Rachael, III |
4895790 | January 23, 1990 | Swanson et al. |
4896030 | January 23, 1990 | Miyaji |
4900133 | February 13, 1990 | Berman |
4907870 | March 13, 1990 | Brucker |
4910591 | March 20, 1990 | Petrossian et al. |
4916374 | April 10, 1990 | Schierbeek |
4917477 | April 17, 1990 | Bechtel et al. |
4931937 | June 5, 1990 | Kakinami et al. |
4937796 | June 26, 1990 | Tendler |
4942533 | July 17, 1990 | Kakinami et al. |
4953305 | September 4, 1990 | Van Lente et al. |
4956591 | September 11, 1990 | Schierbeek |
4961625 | October 9, 1990 | Wood et al. |
4966441 | October 30, 1990 | Conner |
4967319 | October 30, 1990 | Seko |
4970653 | November 13, 1990 | Kenue |
4971430 | November 20, 1990 | Lynas |
4974078 | November 27, 1990 | Tsai |
4987357 | January 22, 1991 | Masaki |
4987410 | January 22, 1991 | Berman et al. |
4991054 | February 5, 1991 | Walters |
5001558 | March 19, 1991 | Burley et al. |
5003288 | March 26, 1991 | Wilhelm |
5012082 | April 30, 1991 | Watanabe |
5016977 | May 21, 1991 | Baude et al. |
5027001 | June 25, 1991 | Torbert |
5027200 | June 25, 1991 | Petrossian et al. |
5044706 | September 3, 1991 | Chen |
5050966 | September 24, 1991 | Berman |
5055668 | October 8, 1991 | French |
5059877 | October 22, 1991 | Teder |
5064274 | November 12, 1991 | Alten |
5072154 | December 10, 1991 | Chen |
5075768 | December 24, 1991 | Wirtz et al. |
5086253 | February 4, 1992 | Lawler |
5096287 | March 17, 1992 | Kakinami et al. |
5097362 | March 17, 1992 | Lynas |
5121200 | June 9, 1992 | Choi |
5124549 | June 23, 1992 | Michaels et al. |
5128874 | July 7, 1992 | Bhanu et al. |
5130709 | July 14, 1992 | Toyama et al. |
5148014 | September 15, 1992 | Lynam |
5166681 | November 24, 1992 | Bottesch et al. |
5168378 | December 1, 1992 | Black |
5170374 | December 8, 1992 | Shimohigashi et al. |
5172235 | December 15, 1992 | Wilm et al. |
5172317 | December 15, 1992 | Asanuma et al. |
5177606 | January 5, 1993 | Koshizawa |
5177685 | January 5, 1993 | Davis et al. |
5182502 | January 26, 1993 | Slotkowski et al. |
5184956 | February 9, 1993 | Langlais et al. |
5189561 | February 23, 1993 | Hong |
5193000 | March 9, 1993 | Lipton et al. |
5193029 | March 9, 1993 | Schofield |
5204778 | April 20, 1993 | Bechtel |
5208701 | May 4, 1993 | Maeda |
5208750 | May 4, 1993 | Kurami et al. |
5214408 | May 25, 1993 | Asayama |
5243524 | September 7, 1993 | Ishida et al. |
5245422 | September 14, 1993 | Borcherts et al. |
5253109 | October 12, 1993 | O'Farrell et al. |
5276389 | January 4, 1994 | Levers |
5285060 | February 8, 1994 | Larson et al. |
5289182 | February 22, 1994 | Brillard et al. |
5289321 | February 22, 1994 | Secor |
5304980 | April 19, 1994 | Maekawa |
5305012 | April 19, 1994 | Faris |
5307136 | April 26, 1994 | Saneyoshi |
5309137 | May 3, 1994 | Kajiwara |
5313072 | May 17, 1994 | Vachss |
5325096 | June 28, 1994 | Pakett |
5325386 | June 28, 1994 | Jewell et al. |
5329206 | July 12, 1994 | Slotkowski et al. |
5331312 | July 19, 1994 | Kudoh |
5336980 | August 9, 1994 | Levers |
5341437 | August 23, 1994 | Nakayama |
5343206 | August 30, 1994 | Ansaldi et al. |
5351044 | September 27, 1994 | Mathur et al. |
5355118 | October 11, 1994 | Fukuhara |
5359666 | October 25, 1994 | Nakayama et al. |
5365603 | November 15, 1994 | Karmann |
5369590 | November 29, 1994 | Karasudani |
5374852 | December 20, 1994 | Parkes |
5386285 | January 31, 1995 | Asayama |
5394333 | February 28, 1995 | Kao |
5406395 | April 11, 1995 | Wilson et al. |
5408346 | April 18, 1995 | Trissel et al. |
5410346 | April 25, 1995 | Saneyoshi et al. |
5414257 | May 9, 1995 | Stanton |
5414461 | May 9, 1995 | Kishi et al. |
5416313 | May 16, 1995 | Larson et al. |
5416318 | May 16, 1995 | Hegyi |
5416478 | May 16, 1995 | Morinaga |
5424952 | June 13, 1995 | Asayama |
5426294 | June 20, 1995 | Kobayashi et al. |
5430431 | July 4, 1995 | Nelson |
5434407 | July 18, 1995 | Bauer et al. |
5440428 | August 8, 1995 | Hegg et al. |
5444478 | August 22, 1995 | Lelong et al. |
5448484 | September 5, 1995 | Bullock et al. |
5451822 | September 19, 1995 | Bechtel et al. |
5457493 | October 10, 1995 | Leddy et al. |
5461357 | October 24, 1995 | Yoshioka et al. |
5461361 | October 24, 1995 | Moore |
5469298 | November 21, 1995 | Suman et al. |
5471515 | November 28, 1995 | Fossum et al. |
5475494 | December 12, 1995 | Nishida et al. |
5487116 | January 23, 1996 | Nakano et al. |
5498866 | March 12, 1996 | Bendicks et al. |
5500766 | March 19, 1996 | Stonecypher |
5510983 | April 23, 1996 | Iino |
5515448 | May 7, 1996 | Nishitani |
5521633 | May 28, 1996 | Nakajima et al. |
5528698 | June 18, 1996 | Kamei et al. |
5529138 | June 25, 1996 | Shaw et al. |
5530240 | June 25, 1996 | Larson et al. |
5530420 | June 25, 1996 | Tsuchiya et al. |
5535144 | July 9, 1996 | Kise |
5535314 | July 9, 1996 | Alves et al. |
5537003 | July 16, 1996 | Bechtel et al. |
5539397 | July 23, 1996 | Asanuma et al. |
5541590 | July 30, 1996 | Nishio |
5550677 | August 27, 1996 | Schofield et al. |
5555312 | September 10, 1996 | Shima et al. |
5555555 | September 10, 1996 | Sato et al. |
5559695 | September 24, 1996 | Daily |
5568027 | October 22, 1996 | Teder |
5574443 | November 12, 1996 | Hsieh |
5581464 | December 3, 1996 | Woll et al. |
5594222 | January 14, 1997 | Caldwell |
5614788 | March 25, 1997 | Mullins |
5617085 | April 1, 1997 | Tsutsumi et al. |
5619370 | April 8, 1997 | Guinosso |
5634709 | June 3, 1997 | Iwama |
5638116 | June 10, 1997 | Shimoura et al. |
5642093 | June 24, 1997 | Kinoshita et al. |
5642299 | June 24, 1997 | Hardin et al. |
5648835 | July 15, 1997 | Uzawa |
5650944 | July 22, 1997 | Kise |
5660454 | August 26, 1997 | Mori et al. |
5661303 | August 26, 1997 | Teder |
5666028 | September 9, 1997 | Bechtel et al. |
5668663 | September 16, 1997 | Varaprasad et al. |
5670935 | September 23, 1997 | Schofield et al. |
5675489 | October 7, 1997 | Pomerleau |
5677851 | October 14, 1997 | Kingdon et al. |
5699044 | December 16, 1997 | Van Lente et al. |
5715093 | February 3, 1998 | Schierbeek et al. |
5724187 | March 3, 1998 | Varaprasad et al. |
5724316 | March 3, 1998 | Brunts |
5737226 | April 7, 1998 | Olson et al. |
5745310 | April 28, 1998 | Mathieu |
5757949 | May 26, 1998 | Kinoshita et al. |
5760826 | June 2, 1998 | Nayer |
5760828 | June 2, 1998 | Cortes |
5760931 | June 2, 1998 | Saburi et al. |
5760962 | June 2, 1998 | Schofield et al. |
5761094 | June 2, 1998 | Olson et al. |
5765116 | June 9, 1998 | Wilson-Jones et al. |
5781437 | July 14, 1998 | Wiemer et al. |
5786772 | July 28, 1998 | Schofield et al. |
5790403 | August 4, 1998 | Nakayama |
5790973 | August 4, 1998 | Blaker et al. |
5793308 | August 11, 1998 | Rosinski et al. |
5793420 | August 11, 1998 | Schmidt |
5796094 | August 18, 1998 | Schofield et al. |
5798575 | August 25, 1998 | O'Farrell et al. |
5835255 | November 10, 1998 | Miles |
5837994 | November 17, 1998 | Stam et al. |
5844505 | December 1, 1998 | Van Ryzin |
5844682 | December 1, 1998 | Kiyomoto et al. |
5845000 | December 1, 1998 | Breed et al. |
5848802 | December 15, 1998 | Breed et al. |
5850176 | December 15, 1998 | Kinoshita et al. |
5850254 | December 15, 1998 | Takano et al. |
5867591 | February 2, 1999 | Onda |
5877707 | March 2, 1999 | Kowalick |
5877897 | March 2, 1999 | Schofield et al. |
5878370 | March 2, 1999 | Olson |
5883739 | March 16, 1999 | Ashihara et al. |
5884212 | March 16, 1999 | Lion |
5890021 | March 30, 1999 | Onoda |
5890083 | March 30, 1999 | Franke et al. |
5892855 | April 6, 1999 | Kakinami et al. |
5896085 | April 20, 1999 | Mori et al. |
5899956 | May 4, 1999 | Chan |
5904725 | May 18, 1999 | Iisaka et al. |
5914815 | June 22, 1999 | Bos |
5920367 | July 6, 1999 | Kajimoto et al. |
5923027 | July 13, 1999 | Stam et al. |
5929784 | July 27, 1999 | Kawaziri et al. |
5929786 | July 27, 1999 | Schofield et al. |
5940120 | August 17, 1999 | Frankhouse et al. |
5949331 | September 7, 1999 | Schofield et al. |
5956181 | September 21, 1999 | Lin |
5959367 | September 28, 1999 | O'Farrell et al. |
5959555 | September 28, 1999 | Furuta |
5963247 | October 5, 1999 | Banitt |
5964822 | October 12, 1999 | Alland et al. |
5971552 | October 26, 1999 | O'Farrell et al. |
5986796 | November 16, 1999 | Miles |
5990469 | November 23, 1999 | Bechtel et al. |
5990649 | November 23, 1999 | Nagao et al. |
6001486 | December 14, 1999 | Varaprasad et al. |
6005492 | December 21, 1999 | Tamura et al. |
6009336 | December 28, 1999 | Harris et al. |
6009337 | December 28, 1999 | Väisänen et al. |
6020704 | February 1, 2000 | Buschur |
6044321 | March 28, 2000 | Nakamura et al. |
6049171 | April 11, 2000 | Stam et al. |
6049619 | April 11, 2000 | Anandan et al. |
6052124 | April 18, 2000 | Stein et al. |
6066933 | May 23, 2000 | Ponziana |
6084519 | July 4, 2000 | Coulling et al. |
6087953 | July 11, 2000 | DeLine et al. |
6091833 | July 18, 2000 | Yasui et al. |
6097023 | August 1, 2000 | Schofield et al. |
6097024 | August 1, 2000 | Stam et al. |
6100811 | August 8, 2000 | Hsu et al. |
6104552 | August 15, 2000 | Thau et al. |
6116743 | September 12, 2000 | Hoek |
6124647 | September 26, 2000 | Marcus et al. |
6124886 | September 26, 2000 | DeLine et al. |
6139172 | October 31, 2000 | Bos et al. |
6144022 | November 7, 2000 | Tenenbaum et al. |
6169940 | January 2, 2001 | Jitsukata et al. |
6172613 | January 9, 2001 | DeLine et al. |
6173222 | January 9, 2001 | Seo et al. |
6175164 | January 16, 2001 | O'Farrell et al. |
6175300 | January 16, 2001 | Kendrick |
6198409 | March 6, 2001 | Schofield et al. |
6201236 | March 13, 2001 | Juds |
6201642 | March 13, 2001 | Bos |
6218960 | April 17, 2001 | Ishikawa et al. |
6222447 | April 24, 2001 | Schofield et al. |
6222460 | April 24, 2001 | DeLine et al. |
6226061 | May 1, 2001 | Tagusa |
6226389 | May 1, 2001 | Lemelson et al. |
6226592 | May 1, 2001 | Luckscheiter et al. |
6243003 | June 5, 2001 | DeLine et al. |
6246961 | June 12, 2001 | Sasaki et al. |
6249214 | June 19, 2001 | Kashiwazaki |
6250148 | June 26, 2001 | Lynam |
6259412 | July 10, 2001 | Duroux |
6259423 | July 10, 2001 | Tokito et al. |
6266082 | July 24, 2001 | Yonezawa et al. |
6266442 | July 24, 2001 | Laumeyer et al. |
6269308 | July 31, 2001 | Kodaka et al. |
6278377 | August 21, 2001 | DeLine et al. |
6282483 | August 28, 2001 | Yano et al. |
6285393 | September 4, 2001 | Shimoura et al. |
6285778 | September 4, 2001 | Nakajima et al. |
6291906 | September 18, 2001 | Marcus et al. |
6292111 | September 18, 2001 | Ishikawa et al. |
6292752 | September 18, 2001 | Franke et al. |
6294989 | September 25, 2001 | Schofield et al. |
6297781 | October 2, 2001 | Turnbull et al. |
6302545 | October 16, 2001 | Schofield et al. |
6310611 | October 30, 2001 | Caldwell |
6313454 | November 6, 2001 | Bos et al. |
6317057 | November 13, 2001 | Lee |
6320176 | November 20, 2001 | Schofield et al. |
6320282 | November 20, 2001 | Caldwell |
6326613 | December 4, 2001 | Heslin et al. |
6329925 | December 11, 2001 | Skiver et al. |
6330511 | December 11, 2001 | Ogura et al. |
6333759 | December 25, 2001 | Mazzilli |
6341523 | January 29, 2002 | Lynam |
6353392 | March 5, 2002 | Schofield et al. |
6366213 | April 2, 2002 | DeLine et al. |
6370329 | April 9, 2002 | Teuchert |
6396397 | May 28, 2002 | Bos et al. |
6411204 | June 25, 2002 | Bloomfield et al. |
6411328 | June 25, 2002 | Franke et al. |
6420975 | July 16, 2002 | DeLine et al. |
6424273 | July 23, 2002 | Gutta et al. |
6428172 | August 6, 2002 | Hutzel et al. |
6430303 | August 6, 2002 | Naoi et al. |
6433676 | August 13, 2002 | DeLine et al. |
6433817 | August 13, 2002 | Guerra |
6442465 | August 27, 2002 | Breed et al. |
6445287 | September 3, 2002 | Schofield et al. |
6477464 | November 5, 2002 | McCarthy et al. |
6485155 | November 26, 2002 | Duroux et al. |
6497503 | December 24, 2002 | Dassanayake et al. |
6498620 | December 24, 2002 | Schofield et al. |
6513252 | February 4, 2003 | Schierbeek et al. |
6515378 | February 4, 2003 | Drummond et al. |
6516664 | February 11, 2003 | Lynam |
6523964 | February 25, 2003 | Schofield et al. |
6534884 | March 18, 2003 | Marcus et al. |
6539306 | March 25, 2003 | Turnbull |
6542840 | April 1, 2003 | Okamoto et al. |
6547133 | April 15, 2003 | DeVries, Jr. et al. |
6553130 | April 22, 2003 | Lemelson et al. |
6559435 | May 6, 2003 | Schofield et al. |
6570998 | May 27, 2003 | Ohtsuka et al. |
6574033 | June 3, 2003 | Chui et al. |
6578017 | June 10, 2003 | Ebersole et al. |
6580996 | June 17, 2003 | Friedrich |
6587573 | July 1, 2003 | Stam et al. |
6589625 | July 8, 2003 | Kothari et al. |
6590719 | July 8, 2003 | Bos |
6593011 | July 15, 2003 | Liu et al. |
6593565 | July 15, 2003 | Heslin et al. |
6593698 | July 15, 2003 | Stam et al. |
6594583 | July 15, 2003 | Ogura et al. |
6611202 | August 26, 2003 | Schofield et al. |
6611610 | August 26, 2003 | Stam et al. |
6627918 | September 30, 2003 | Getz et al. |
6631316 | October 7, 2003 | Stam et al. |
6631994 | October 14, 2003 | Suzuki et al. |
6636258 | October 21, 2003 | Strumolo |
6648477 | November 18, 2003 | Hutzel et al. |
6650233 | November 18, 2003 | DeLine et al. |
6650455 | November 18, 2003 | Miles |
6671607 | December 30, 2003 | Ishizu et al. |
6672731 | January 6, 2004 | Schnell et al. |
6674562 | January 6, 2004 | Miles |
6678056 | January 13, 2004 | Downs |
6678614 | January 13, 2004 | McCarthy et al. |
6680792 | January 20, 2004 | Miles |
6690268 | February 10, 2004 | Schofield et al. |
6691008 | February 10, 2004 | Kondo et al. |
6700605 | March 2, 2004 | Toyoda et al. |
6703925 | March 9, 2004 | Steffel |
6704621 | March 9, 2004 | Stein et al. |
6708100 | March 16, 2004 | Russell et al. |
6710908 | March 23, 2004 | Miles et al. |
6711474 | March 23, 2004 | Treyz et al. |
6714331 | March 30, 2004 | Lewis et al. |
6717610 | April 6, 2004 | Bos et al. |
6735506 | May 11, 2004 | Breed et al. |
6741377 | May 25, 2004 | Miles |
6744353 | June 1, 2004 | Sjönell |
6748312 | June 8, 2004 | Russell et al. |
6757109 | June 29, 2004 | Bos |
6762867 | July 13, 2004 | Lippert et al. |
6794119 | September 21, 2004 | Miles |
6795221 | September 21, 2004 | Urey |
6802617 | October 12, 2004 | Schofield et al. |
6806452 | October 19, 2004 | Bos et al. |
6807287 | October 19, 2004 | Hermans |
6813371 | November 2, 2004 | Kakinami |
6822563 | November 23, 2004 | Bos et al. |
6823241 | November 23, 2004 | Shirato et al. |
6824281 | November 30, 2004 | Schofield et al. |
6831261 | December 14, 2004 | Schofield et al. |
6847487 | January 25, 2005 | Burgner |
6864930 | March 8, 2005 | Matsushita et al. |
6882287 | April 19, 2005 | Schofield |
6889161 | May 3, 2005 | Winner et al. |
6891563 | May 10, 2005 | Schofield et al. |
6909753 | June 21, 2005 | Meehan et al. |
6917693 | July 12, 2005 | Kiridena |
6941216 | September 6, 2005 | Isogai et al. |
6946978 | September 20, 2005 | Schofield |
6953253 | October 11, 2005 | Schofield et al. |
6968266 | November 22, 2005 | Ahmed-Zaid et al. |
6968736 | November 29, 2005 | Lynam |
6975775 | December 13, 2005 | Rykowski et al. |
7004593 | February 28, 2006 | Weller et al. |
7004606 | February 28, 2006 | Schofield |
7005974 | February 28, 2006 | McMahon et al. |
7038577 | May 2, 2006 | Pawlicki et al. |
7046448 | May 16, 2006 | Burgner |
7049945 | May 23, 2006 | Breed et al. |
7062300 | June 13, 2006 | Kim |
7065432 | June 20, 2006 | Moisel et al. |
7085637 | August 1, 2006 | Breed et al. |
7092548 | August 15, 2006 | Laumeyer et al. |
7113867 | September 26, 2006 | Stein |
7116246 | October 3, 2006 | Winter et al. |
7123168 | October 17, 2006 | Schofield |
7133661 | November 7, 2006 | Hatae et al. |
7149613 | December 12, 2006 | Stam et al. |
7151844 | December 19, 2006 | Stevenson et al. |
7151996 | December 19, 2006 | Stein |
7167796 | January 23, 2007 | Taylor et al. |
7195381 | March 27, 2007 | Lynam et al. |
7202776 | April 10, 2007 | Breed |
7224324 | May 29, 2007 | Quist et al. |
7227459 | June 5, 2007 | Bos et al. |
7227611 | June 5, 2007 | Hull et al. |
7249860 | July 31, 2007 | Kulas et al. |
7253723 | August 7, 2007 | Lindahl et al. |
7255451 | August 14, 2007 | McCabe et al. |
7295682 | November 13, 2007 | Otsuka et al. |
7311406 | December 25, 2007 | Schofield et al. |
7325934 | February 5, 2008 | Schofield et al. |
7325935 | February 5, 2008 | Schofield et al. |
7338177 | March 4, 2008 | Lynam |
7339149 | March 4, 2008 | Schofield et al. |
7344261 | March 18, 2008 | Schofield et al. |
7360932 | April 22, 2008 | Uken et al. |
7370983 | May 13, 2008 | DeWind et al. |
7375803 | May 20, 2008 | Bamji |
7380948 | June 3, 2008 | Schofield et al. |
7388182 | June 17, 2008 | Schofield et al. |
7402786 | July 22, 2008 | Schofield et al. |
7423248 | September 9, 2008 | Schofield et al. |
7423821 | September 9, 2008 | Bechtel et al. |
7425076 | September 16, 2008 | Schofield et al. |
7459664 | December 2, 2008 | Schofield et al. |
7463138 | December 9, 2008 | Pawlicki et al. |
7502048 | March 10, 2009 | Okamoto |
7526103 | April 28, 2009 | Schofield et al. |
7541743 | June 2, 2009 | Salmeen et al. |
7561181 | July 14, 2009 | Schofield et al. |
7565006 | July 21, 2009 | Stam et al. |
7566851 | July 28, 2009 | Stein et al. |
7605856 | October 20, 2009 | Imoto |
7616781 | November 10, 2009 | Schofield et al. |
7619508 | November 17, 2009 | Lynam et al. |
7633383 | December 15, 2009 | Dunsmoir et al. |
7639149 | December 29, 2009 | Katoh |
7655894 | February 2, 2010 | Schofield et al. |
7676087 | March 9, 2010 | Dhua et al. |
7720580 | May 18, 2010 | Higgins-Luthman |
7729580 | June 1, 2010 | Tanaka et al. |
7786898 | August 31, 2010 | Stein et al. |
7792329 | September 7, 2010 | Schofield et al. |
7843451 | November 30, 2010 | Lafon |
7855778 | December 21, 2010 | Yung et al. |
7859565 | December 28, 2010 | Schofield et al. |
7877175 | January 25, 2011 | Higgins-Luthman |
7881496 | February 1, 2011 | Camilleri |
7914187 | March 29, 2011 | Higgins-Luthman et al. |
7914188 | March 29, 2011 | DeLine et al. |
7930160 | April 19, 2011 | Hosagrahara et al. |
7949486 | May 24, 2011 | Denny et al. |
7991522 | August 2, 2011 | Higgins-Luthman |
7994462 | August 9, 2011 | Schofield et al. |
8009977 | August 30, 2011 | Matsuoka et al. |
8017898 | September 13, 2011 | Lu et al. |
8064643 | November 22, 2011 | Stein et al. |
8082101 | December 20, 2011 | Stein et al. |
8095310 | January 10, 2012 | Taylor et al. |
8098142 | January 17, 2012 | Schofield et al. |
8100568 | January 24, 2012 | DeLine et al. |
8164628 | April 24, 2012 | Stein et al. |
8203440 | June 19, 2012 | Schofield et al. |
8218007 | July 10, 2012 | Lee et al. |
8222588 | July 17, 2012 | Schofield et al. |
8224031 | July 17, 2012 | Saito |
8233045 | July 31, 2012 | Luo et al. |
8254635 | August 28, 2012 | Stein et al. |
8300886 | October 30, 2012 | Hoffmann |
8314689 | November 20, 2012 | Schofield et al. |
8324552 | December 4, 2012 | Schofield et al. |
8378851 | February 19, 2013 | Stein et al. |
8386114 | February 26, 2013 | Higgins-Luthman |
8421865 | April 16, 2013 | Euler et al. |
8452055 | May 28, 2013 | Stein et al. |
8514282 | August 20, 2013 | Imanishi et al. |
8534887 | September 17, 2013 | DeLine et al. |
8553088 | October 8, 2013 | Stein et al. |
9014904 | April 21, 2015 | Higgins-Luthman |
9193303 | November 24, 2015 | Higgins-Luthman |
9446713 | September 20, 2016 | Lu et al. |
9469250 | October 18, 2016 | Lynam |
9493072 | November 15, 2016 | Goto et al. |
9796332 | October 24, 2017 | Lu et al. |
9802542 | October 31, 2017 | Lu et al. |
9815408 | November 14, 2017 | Bonhoure et al. |
9900522 | February 20, 2018 | Lu |
9940528 | April 10, 2018 | Higgins-Luthman |
9950738 | April 24, 2018 | Lu et al. |
20010012985 | August 9, 2001 | Okamoto et al. |
20020005778 | January 17, 2002 | Breed |
20020011611 | January 31, 2002 | Huang et al. |
20020080235 | June 27, 2002 | Jeon |
20020113873 | August 22, 2002 | Williams |
20020167589 | November 14, 2002 | Schofield et al. |
20030052773 | March 20, 2003 | Sjonell |
20030103142 | June 5, 2003 | Hitomi et al. |
20030137586 | July 24, 2003 | Lewellen |
20030156015 | August 21, 2003 | Winner et al. |
20030169522 | September 11, 2003 | Schofield et al. |
20030222982 | December 4, 2003 | Hamdan et al. |
20040016870 | January 29, 2004 | Pawlicki |
20040164228 | August 26, 2004 | Fogg et al. |
20050083427 | April 21, 2005 | Imoto |
20050219852 | October 6, 2005 | Stam et al. |
20050237385 | October 27, 2005 | Kosaka et al. |
20060018511 | January 26, 2006 | Stam et al. |
20060018512 | January 26, 2006 | Stam et al. |
20060050018 | March 9, 2006 | Hutzel et al. |
20060091813 | May 4, 2006 | Stam et al. |
20060103727 | May 18, 2006 | Tseng |
20060250501 | November 9, 2006 | Wldmann et al. |
20070024724 | February 1, 2007 | Stein et al. |
20070104476 | May 10, 2007 | Yasutomi et al. |
20070109406 | May 17, 2007 | Schofield et al. |
20070120657 | May 31, 2007 | Schofield et al. |
20070242339 | October 18, 2007 | Bradley |
20080043099 | February 21, 2008 | Stein et al. |
20080147321 | June 19, 2008 | Howard et al. |
20080192132 | August 14, 2008 | Bechtel et al. |
20080266396 | October 30, 2008 | Stein |
20090113509 | April 30, 2009 | Tseng et al. |
20090160987 | June 25, 2009 | Bechtel et al. |
20090190015 | July 30, 2009 | Bechtel et al. |
20090256938 | October 15, 2009 | Bechtel et al. |
20090290032 | November 26, 2009 | Zhang et al. |
20110216201 | September 8, 2011 | McAndrew et al. |
20120045112 | February 23, 2012 | Lundblad et al. |
20120069185 | March 22, 2012 | Stein |
20120176528 | July 12, 2012 | Denny et al. |
20120200707 | August 9, 2012 | Stein et al. |
20120314071 | December 13, 2012 | Rosenbaum et al. |
20120320209 | December 20, 2012 | Vico |
20130141580 | June 6, 2013 | Stein et al. |
20130147957 | June 13, 2013 | Stein |
20130169812 | July 4, 2013 | Lu et al. |
20130286193 | October 31, 2013 | Pflug |
20130321615 | December 5, 2013 | Schofield |
20140022378 | January 23, 2014 | Higgins-Luthman |
20140043473 | February 13, 2014 | Rathi et al. |
20140063254 | March 6, 2014 | Shi et al. |
20140098229 | April 10, 2014 | Lu et al. |
20140247352 | September 4, 2014 | Rathi et al. |
20140247354 | September 4, 2014 | Knudsen |
20140320658 | October 30, 2014 | Pliefke |
20140333729 | November 13, 2014 | Pflug |
20140347486 | November 27, 2014 | Okouneva |
20140350834 | November 27, 2014 | Turk |
20150085121 | March 26, 2015 | Englander |
20150217694 | August 6, 2015 | Bingle et al. |
2133182 | January 1973 | DE |
2808260 | August 1979 | DE |
2931368 | February 1981 | DE |
2946561 | May 1981 | DE |
3041692 | May 1981 | DE |
3248511 | July 1984 | DE |
3041612 | August 1986 | DE |
4107965 | September 1991 | DE |
4118208 | November 1991 | DE |
4139515 | June 1992 | DE |
4123641 | January 1993 | DE |
4124654 | January 1993 | DE |
10115043 | March 2001 | DE |
0048810 | April 1982 | EP |
0202460 | November 1986 | EP |
0830267 | March 1988 | EP |
0353200 | January 1990 | EP |
0354261 | February 1990 | EP |
0416222 | June 1990 | EP |
0527665 | February 1991 | EP |
0426503 | May 1991 | EP |
0450553 | October 1991 | EP |
0492591 | July 1992 | EP |
0513476 | November 1992 | EP |
0361914 | February 1993 | EP |
0591743 | April 1994 | EP |
0605045 | July 1994 | EP |
0640903 | March 1995 | EP |
0697641 | February 1996 | EP |
0755524 | January 1997 | EP |
0788947 | August 1997 | EP |
1074430 | February 2001 | EP |
1115250 | July 2001 | EP |
1120746 | August 2001 | EP |
1339228 | August 2003 | EP |
2377094 | October 2011 | EP |
2667325 | November 2013 | EP |
2241085 | March 1975 | FR |
2513198 | March 1983 | FR |
2585991 | February 1987 | FR |
2641237 | June 1990 | FR |
2672857 | August 1992 | FR |
2673499 | September 1992 | FR |
2726144 | April 1996 | FR |
934037 | August 1963 | GB |
1535182 | December 1978 | GB |
2029343 | March 1980 | GB |
2119087 | November 1983 | GB |
2137373 | October 1984 | GB |
2137573 | October 1984 | GB |
2156295 | October 1985 | GB |
2233530 | September 1991 | GB |
2244187 | November 1991 | GB |
2255539 | November 1992 | GB |
2327823 | February 1999 | GB |
S5539843 | March 1980 | JP |
57173801 | October 1982 | JP |
57208530 | December 1982 | JP |
57208531 | December 1982 | JP |
S58110334 | June 1983 | JP |
58209635 | December 1983 | JP |
59114139 | July 1984 | JP |
59133336 | July 1984 | JP |
6079889 | May 1985 | JP |
6080953 | May 1985 | JP |
60212730 | October 1985 | JP |
60261275 | December 1985 | JP |
6243543 | February 1987 | JP |
6216073 | April 1987 | JP |
S6216073 | April 1987 | JP |
6272245 | May 1987 | JP |
62122487 | June 1987 | JP |
62122844 | June 1987 | JP |
S62131837 | June 1987 | JP |
6414700 | January 1989 | JP |
02117935 | March 1989 | JP |
01123587 | May 1989 | JP |
H1168538 | July 1989 | JP |
H236417 | August 1990 | JP |
371399 | March 1991 | JP |
03099952 | April 1991 | JP |
3099952 | April 1991 | JP |
04239400 | November 1991 | JP |
03284413 | December 1991 | JP |
H0424132 | January 1992 | JP |
H0424132 | January 1992 | JP |
04114587 | April 1992 | JP |
H04127280 | April 1992 | JP |
04245886 | September 1992 | JP |
0577657 | March 1993 | JP |
05050883 | March 1993 | JP |
55000883 | March 1993 | JP |
05213113 | August 1993 | JP |
5213113 | August 1993 | JP |
H05189694 | September 1993 | JP |
H05189694 | September 1993 | JP |
06107035 | April 1994 | JP |
06156638 | June 1994 | JP |
6227318 | August 1994 | JP |
06267304 | September 1994 | JP |
06276524 | September 1994 | JP |
06295601 | October 1994 | JP |
07004170 | January 1995 | JP |
0732936 | February 1995 | JP |
0747878 | February 1995 | JP |
07052706 | February 1995 | JP |
0769125 | March 1995 | JP |
07105496 | April 1995 | JP |
730149 | June 1995 | JP |
08166221 | June 1996 | JP |
2630604 | July 1997 | JP |
200274339 | March 2002 | JP |
2002232869 | August 2002 | JP |
200350107 | February 2003 | JP |
200376987 | March 2003 | JP |
2003083742 | March 2003 | JP |
2003329411 | November 2003 | JP |
2003329411 | November 2003 | JP |
20041658 | January 2004 | JP |
WO1994019212 | February 1994 | WO |
WO1994/27262 | November 1994 | WO |
WO1996021581 | July 1996 | WO |
WO1996038319 | December 1996 | WO |
WO1998/14974 | April 1998 | WO |
WO1999/14088 | March 1999 | WO |
WO1999/23828 | May 1999 | WO |
WO2012139636 | October 2012 | WO |
WO2012139660 | October 2012 | WO |
WO2012143036 | October 2012 | WO |
- Stein et al., “Stereo-Assist: Top-down Stereo for Driver Assistance Systems”.
- Taktak et al., “Vehicle Detection at Night Using Image Processing and Pattern Recognition” IEEE 1994.
- Gordenker, “Fender Mirrors”, The Japan Times webpage Nov. 18, 2013.
- Thibault, “360° Vision System: Opportunities in Transportation” ImmerVision, Canada.
- Roulet et al., Multi-task single lens for automotive vision applications: ImmerVision, Proc. of SPIE vol. 731-4, Canada.
- Thibault, “Novel Compact Panomorph Lens Based Vision System for Monitoring Around a Vehicle” ImmerVision.
- Valeo webpage, “360 Vue® multi-camera system: perfect view of the vehicle's environment” https://www.valeo.com/en/360-vue, retrieved Jul. 25, 2018.
- Valeo webpage, “12PM1 Parkman ECU User Manual 12PM1_FCC, IC_ Valeo Schalter und Sensoren GmbH” https://fccid.io/VE2-12PM1/UserManual-pdf-2552374, retrieved Jul. 25, 2018.
- Valeo Infos News, “Valeo Recieves Innovation Prize at Automechanika for its Lane Change Surveillance System” Paris, France, Sep. 14, 2004.
- Valeo webpage, “Our Activities” https://www.valeo.com/en/our-activities/, retrieved Jul. 25, 2018.
- Valeo webpage, “Research & Development” https://www.valeo.com/en/research-development/, retrieved Jul. 25, 2018.
- Valeo article, “Valeo's LaneVue™ Systems Takes an Automotive News PACE Award 2005”, Paris, France, Apr. 12, 2005.
- Gardner, “After the battle: as smoke clears, mirror makers arent't looking back” Ward's Auto webpage article, Dec. 1, 1996, https://www.wardsauto.com/news-analysis/after-battle-smoke-clears-mirror-makers-arent-looking-back, retrieved Aug. 14, 2018.
- Murphy, “Looking Back to the Future—How hard can it be to eliminate a driver's blindspot?” Wards Auto webpage, May 1, 1998, https://www.wardsauto.com/news-analysis/looking-back-future-how-hard-can-it-be-eliminate-drivers-blindspot., retrieved Aug. 14, 2018.
- Murphy, “Magna Attacking Vehicle Blindspots” Wards Auto webpage, Feb. 23, 2007, https://www.wardsauto.com/news-analysis/magna-attacking-vehicle-blindspots, retrieved Aug. 14, 2018.
- Adasens Automotive GmbH website article, “Machine Vision Forward Looking Cameras—Vehicle Functions,” http://www.adasens.com/Vision.html., retrieved Jul. 25, 2018.
- Adasens Automotive GmbH website article, “Safety at every lane change,” http://www.adasens.com/news_details/safety-at-every-lane-change.html, retrieved Jul. 25, 2018.
- AggNet website article, “A big step in all-round visibility,” https://www.agg-net.com/news/a-big-forward-step-in-all-round-visibility[Aug. 15, 2018 7:28:54 PM].
- Ambarella, “A9AQ Automotive Multi-Camera Electronic Mirror and Surround View SoC,” Product Brief.
- Ambarella Camera Products website, “Automotive Camera Products” http://www.ambarella.com/products/automotive-cameras/automotive-camera-products[Aug. 15, 2018 6:00:48 PM].
- Ambarella, Embedded Computer Vision SoCs website article, “Ambarella Introducts Autmotive Multi-Camera Electronc Mirror and Surround View SoCs” http://www.ambaralla.com/news/103/122/Ambarella-Introducts-Automotive-Multi-Camera retrieved Jul. 25, 2018.
- ASL iVENCS website article, “Versatile Site or Wide-Area Asset Control System” http:///www.asl-control.co.uk/product/ivencs/ retreived Jul. 25, 2018.
- Auto Evolution website article, “A Short History of Mercedes-Benz Autonomous Driving Technology,” http://www.autoevolution.com/news/a-short-history-of-mercedes-benz-autonomous-driving-technology-68148.html [Aug. 14, 2018 6:24:02 PM].
- Autoblog website article, “2012 Nissan Rogue Gains Optional Around View Monitor [w/video]” http://www.autoblog.com/2011/11/11/2012-nissan-rogue-gains-optional-around-view-monitor, retreived Jul. 25, 2018.
- Autoblog, “Continental's 360-degree camera system goes next level with 3D” http://www.autoblog.com/2014/08/15/continental-360-degree-camera-3d-video/ retrieved Jul. 25, 2018.
- Brown, “Automatic Panoramic Image Stitching using Invariant Features” Department of Computer Science, University of British Columbia, Vancouver, Canada.
- Bigas et al., “Review of CMOS image sensors” Microelectronics Journal 37, www,elsevier.com/locate/mejo, 2006, 433-451.
- Bosch Mobility Solutions website article, “Multi-camera system” https://www.bosch-mobility-solutions.com/ . . . s/passenger-cars-and-light-commericial-vehicles/driver-assistance-systems/multi-camera-systems [Aug. 15, 2018 7:24:09 PM].
- Boyle et al., “Charge Coupled Semiconductor Devices” B.S.T.J. Briefs, Jan. 29, 1970, 587-593.
- Business Wire, “Iteris Technology Chosen by Nissan for Auto Industry's First-Ever Lane Departue Warning System,” Business Wire, Mar. 31, 2004.
- Business Wire, “Logex Corp.Selects Iteris' AutoVue Lane Deparature Warning Technology on New Truck Purchases” Business Wire, Jun. 26, 2003.
- Business Wire, “Renesas Electronics and Cogent Embedded Collaborate to Make 3D Surround View Parking Assist System Standard in All New Cars” Business Wire, https://businesswire.com/news/hone/20170913006643/en.retrieved Jul. 25, 2018.
- Busworld, “No Blind Spot Continental ProViu ASL360 360-Degree Camera System for Retrofitting” https://www.busworld.org/articles/detail/1986[Aug. 13, 2018 3:55:01 PM].
- Cadillac website article, “Cadillac CT6 Features Industry-first surround-view video recording system” http://media.cadillac.com/media/us/en/cadillac/news/detail.html/content/Pages/news/us/en, retrieved Jul. 25, 2018.
- CAR Magazine website article “New Panoramic 360-degree parking camera unveiled” CAR Magazine, https://www.carmagazine.co.uk/car-news-first-official-pictures/new-panoramic-360 degree. retrieved Jul. 25, 2018.
- Clarion U.S.A. webpage, “Clarion and Porsche Introduced all-surrouind view overhead camera system for Porsche Panamera,” http://www.clarion.com/us/en/corp/information/news-release/2014/1212-1/index.html, Dec. 12, 2014, retrieved Jul. 25, 2018.
- CNET webpage, “1956 Buick Centurion Dream Car” https://www.cnet.com/pictures/photos-when-gms-designs-ruled-the-road/11/ retrieved Aug. 10, 2018.
- CNET webpage, “2011 Range Rover Hands-on: 360-degree view from onboard cameras” https://www.cnet.com/roadshow/news.2011-range-rover-hands-on-360-degree-views retrieved Jul. 25, 2018.
- Surround View-Cogent Embedded website article, “Surround View” https://www.cogentembedded.com/surround-view/ retrieved Jul. 25, 2018.
- Consumer's Reports webpage, “2012 Nissian Rogue adds around view monitor” https://consumerreports.org/cro/news/2011/11/2012-nissan-rogue-adds-arouns-view-monitor/index.htm [Aug. 15, 2018 6:32:33 PM].
- PR Newswire Continental webpage “Continental Acquies ASL Vision, a Specialised Company for 360-Degree Surround Detection,” https://www.prnewswire.com/news-releases/continental-acquires-asl-vision-a-specialized. Jan. 14, 2013.
- Continental Automotive webpage, “Rear View System RVS3XX” https://continental-automotive.com/en-gl/Passenger-Cars/Chassis-Safety/Advanced retrieved Jul. 25, 2018.
- Continental webpage article, “The latest camera generation from Continental gives the on-board computer bigger eyes” https://www.continental-corporation.com/en/press/press/releases/mfc-500-camera. retrieved Jul. 30, 2018.
- Continental Automotive webpage, “Mono Camera, Multi Function Mono Camera” https://www.continental-automotive.com/en/gl/Passenger-Cars-Chasses-Safety/Advanced-Driver-Assistance-Systems/Cameras/Mono-Camera [Aug. 15, 2018 6:40:30 PM].
- Continental Automotive webpage, “ProViuASL360 Greater situational awareness using advanced driver assistance” https://www.continental-automotive.com/ . . . gl/Trucks-Buses/Vehicle-Chassis-Body/Advanced-Driver-Assistance-Systems/Camera-Based-Systems/ProViu360 [Aug. 15, 2018 5:56:23 PM].
- Earnest, “Stanford Cart, How a Moon Rover Project was Blocked by a Politician but got kicked by Football into a Self-Driving Vehicle” https://web.stanford.edu/˜learnest/les/cart.html. Mar. 6, 2018.
- Edmund Optics webpage, “Imaging Electronics 101: Understanding Camera Sensors for Machine Vision Applications” Ittps://www.edmundoptics.com/resources/application-notes/imaging/understanding-camera retrieved Aug. 10, 2018.
- “Electronic Displays and Information Systems”, SAE Global, International Congress and Exposition Detroit Feb. 24-28, 1986.
- Fossum et al., “A Review of the Pinned Photodiode for CCD and CMOS Image Sensors” IEEE Journal of the Electron Devices Society, vol. 2, No. 3, May 2014.
- Frost & Sullivan webpage article, “In Search of the Ideal Blind-Spot Detection System” http://frost.com/sublib/display-market-insight-top.do?id=19917640 [Aug. 14, 2018 10:11:11 PM].
- Guiducci, “Camera Calibration for Road Applications,” Computer vision and Image Understanding 79, 2000, 250-266.
- “History of Photogrammetry”, The Center for Photogrammetric Training, last updated Aug. 24, 2008, 1-36.
- Nolan, “Survey of Electronic Displays,” Automotive Electronics II, Society of Automotive Engineers, Inc., Feb. 1975.
- Batavia et al., “Overtaking Vehicle Detection Using Implicit Optical Flow” IEEE Conference on Intelligent Transportation Systems, Boston Park Plaza Hotel, Boston, MA Nov. 9-12, 1997.
- Korea IT Times, “MCNEX—The Eye of Digital Devices” https///www.koreaitimes.com/printstory/20625/mcnex-eyedigital-devices Jun. 19, 2012.
- Valeo Press Release, “LaneVue System in the running for the finals at the Automotive News PACE Awards 2005” Auburn Hills, MI Oct. 19, 2004.
- Liang et al., “Video Stabilization for a Camcorder Mounted on a Moving Vehicle” IEEE Transactions on Vehicular Technology, vol. 53, No. 6, Nov. 2004.
- Choi et al., “Advanced driver-assistance systems: Challenges and opportunities ahead” McKinsey & Company, Feb. 2016.
- Moravec, “Obstacle avoidance and navigation in the real world by a seeing robot rover” Stanford University, 1980.
- NHTSA Driver Assitance Technologies, “Driver Assistance Technologies” webpage, https://www.nhtsa.gov/equipment/driver-assistance-technologies [Aug. 14, 2018 6:56:18 PM].
- Pomerleau, “RALPH: Rapidly Adapting Lateral Position Handler” The Robotics Instutute, Carnegie Mellong University, 506-511.
- Otsuka et al., “Flat Dot Matrix Display Module for Vehicle Instrumentation” Proceedings of the Fourth International Pacific Conference on Automotive Engineering., Melbourne, Australia, Nov. 8-14, 1987.
- Ortega, “An Interactive, Reconfigurable Display System for Automotive Instrumentation” Society of Automotive Engineers, Inc., 1987.
- Softpedia News webpage article, “Fujitsu Creates a 360-degree wrap-around video of your car's surrounding” https://news.softpedia.com/news.Fujitsu-Creates-a-360-Degree-Wrap-Around-Video-of-Your-Cars-Surrounding, Oct. 20, 2010, retrieved Jul. 25, 2018.
- SRI International webpage, “Shakey the Robot” https://www/.sri.com/work/timeline-/nnocation/timeline.php?timeline=computing-digital, retrieved Aug. 10, 2018.
- Achler et al., “Vehicle Wheel Detector using 2D Filter Banks,” IEEE Intelligent Vehicles Symposium of Jun. 2004.
- Ahmed et al., “Differential Methods for Nonmetric Calibration of Camera Lens Distortion,” Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on. vol. 2. IEEE, 2001.
- Aufrere et al., “A fast and robust vision based road following algorithm,” Proceedings of the IEEE Intelligent Vehicles Symposium 2000, Dearborn, MI, Oct. 3-5, 2000.
- Ballard et al., “Computer Vision”, 1982, p. 88-89, sect. 3.4.1.
- Batavia et al., “Detecting Overtaking Vehicles with Implicit Optical Flow,” Robotics Institute, Carnegie Mellon University, Mar. 4, 1998.
- Behringer et al., “Visuelle Erkennung and Interpretation des Fahrspurver-Laufes durch Rechnersehen fur ein autonomes Strassenfahrzeug”, No. 310.
- Behringer et al., “Simultaneous Estimation of Pitch Angle and Lane Width from the Video Image of a Marked Road,” pp. 966-973, Sep. 12-16, 1994.
- Bertozzi et al., “Obstacle and Lane Detection on the ARGO Autonomous Vehicle,” University of Parma, Italy.
- Bertozzi et al., “Pedestrian Localization and Tracking System with Kalman Filtering,” IEEE Intelligent Vehicles Symposium, University of Parma, Italy, Jun. 14-17, 2004.
- Bertozzi et al., “Vision-based intelligent vehicles: State of the art and perspectives,” Robotics and Autonomous Systems, 2000.
- Birkland, “See and Be Seen”, Management Techniques, Fleet Equipment, May 1993.
- Bishop, “Intelligent Vehicle Applications Worldwide,” Intelligent Transportation Systems, Jan.-Feb. 2000.
- Bogner, “An Introduction to Panosheric Imaging,” IEEE, 1995.
- Bohrer et al., “An Integrated Obstacle Detection Framework for Intelligent Cruise Control on Motorways,” Research Gate Conference Paper, Oct. 1995.
- Borenstein et al., “Where am I? Sensors and Method for Mobile Robot Positioning”, University of Michigan, Apr. 1996, pp. 2, 125-128.
- Bow, Sing T., “Pattern Recognition and Image Preprocessing (Signal Processing and Communications)”, CRC Press, Jan. 15, 2002, pp. 557-559.
- Braess et al., “Prometheus: Vision des ‘intelligenten Automobils’ auf ‘intelligenter Strasse’?” Forschung, Versuch einer kristischen Wurdigung—Teil 1, pp. 200-205.
- Braess et al., “Prometheus: Vision des ‘intelligenten Automobils’ auf ‘intelligenter Strasse’?” Forschung, Versuch einer kristischen Wurdigung—Teil 2, pp. 330-336.
- Brauckmann et al., “Towards All Around Automatic Visual Obstacle Sensing for Cars,” Germany, no date.
- Broggi et al., “Self-Calibration of a Stereo Vision System for Automotive Applications”, Proceedings of the 2001 IEEE International Conference on Robotics & Automation, Seoul, KR, May 21-26, 2001.
- Broggi et al., “Automatic Vehicle Guidance: The Experience of the ARGO Vehicle”, World Scientific Publishing Co., 1999.
- Broggi et al., “Multi-Resolution Vehicle Detection using Artificial Vision,” IEEE Intelligent Vehicles Symposium of Jun. 2004.
- Broggi et al., “Shape-based Pedestrian Detection,” Proceedings of the IEEE Intelligent Vehicles Symposium, 2000.
- Broggi et al., “The Evolution of the PAPRICA System,” Integrated Computer-Aided Engineering 4.2,1997, pp. 114-136.
- Broggi, “Robust Real-Time Lane and Road Detection in Critical Shadow Conditions,” no date.
- Broggi, “Vision-based Road Detection in Automotive Systems: A real-time expectation-driven approach”, 1995.
- Brown, A Survey of Image Registration Techniques, vol. 24, ACM Computing Surveys, pp. 325-376, 1992.
- Brudigam, “Intelligente Fahrmanover sehender autonomer Fahrzeuge in autobahnahnlicher umgebung,” Jun. 22, 1994.
- Buehler et al., “Non-Metric Image-Based Rendering for Video Stabilization,” IEEE, 2001.
- Burger et al., “Estimating 3-D Egomotion from Perspective Image Sequences”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, No. 11, pp. 1040-1058, Nov. 1990.
- Burt et al., A Multiresolution Spline with Application to Image Mosaics, ACM Transactions on Graphics, vol. 2. No. 4, pp. 217-236, Oct. 1983.
- Carter, “Honda's Intelligent Night Vision System”, Oct. 26, 2004.
- Chen, “Image Stitching Comparisons and New Techniques,” Computer Science Dept. of the University of Auckland, Oct. 1998.
- Chicago Sun Times, “Toyota: Cars for today and tomorrow,” Section: Auto Show, Feb. 9, 1992, pp. 1-3.
- Collins et al., “Calibration of an Outdoor Active Camera System,” The Robotics Institute of Carnegie Mellon University, 1998.
- Cucchiara et al., Vehicle Detection under Day and Night Illumination, 1999.
- Denyer et al., “On-Chip CMOS Sensors for VLSI Imaging Systems”, Dept. of Elect. Engineering, University of Edinburgh, pp. 4b1.1-4b1.5, 1991.
- Derutin et al., “Real-time Collision Avoidance at Road-Crossings on board the Prometheus-ProLab 2 Vehicle,” Proceedings of the IEEE Intelligent Vehicle Symposium, 1994, France, pp. 56-61.
- Dickmanns et al., “A Curvature-based Scheme for Improving Road Vehicle Guidance by Computer Vision,” University of Bundeswehr München, 1986.
- Dickmanns et al., “Recursive 3-D road and relative ego-state recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, No. 2, Feb. 1992.
- Dickmanns et al., “Scene Recognition and Navigation Capabilities for Lane Changes and Turns in Vision-Based Vehicle Guidance,” Control Eng. Practice, vol. 4, No. 5, 1996, Great Britain.
- Dickmanns et al., “The seeing passenger car ‘VaMoRs-P’”, Oct. 24, 1994.
- Dickmanns et al.; “An integrated spatio-temporal approach to automatic visual guidance of autonomous vehicles,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 20, No. 6, Nov./Dec. 1990.
- Dickmanns, “Road Vehicle Eyes for High Precision Navigation,” High Precision Navigation, Diimmler Verlag, Bonn, 1995.
- Dickmanns, “4-D dynamic vision for intelligent motion control”, Universitat der Bundeswehr Munich, 1991.
- Dickmanns, “Dynamic Vision for Perception and Control of Motion,” Springer Science & Business Media, 2007, Table of Contents, no date.
- Dickmanns, “The Development of Machine Vision for Road Vehicles in the Last Decade,” Intelligent Vehicle Symposium, 2002. IEEE. vol. 1. IEEE, 2002.
- Dickmanns, “Vehicles Capable of Dynamic Vision: A New Breed of Technical Beings?” Artificial Intelligence, 1998.
- Dickmanns, “Vision for Ground Vehicles: History and Prospects,” Int. of Vehicle Autonomous Systems, vol. 1, No. 1, 2002.
- Donnelly Panoramic Vision™ on Renault Talisman Concept Car At Frankfort Motor Show, PR Newswire, Frankfort, Germany Sep. 10, 2001.
- Efenberger et al., “Automatic Recognition of Vehicles Approaching from Behind,” Proceedings of the Intelligent Vehicles' 92 Symposium, 1992, Detroit, Michigan.
- Enkelmann et al., “Obstacle Detection by Real-Time Optical Flow Evaluation,” Proceedings of the IEEE Intelligent Vehicles' 94 Symposium, 1994.
- Ernst et al., “Camera calibration for lane and obstacle detection” Intelligent Transportation Systems, 1999 pp. 356-361.
- Fintzel et al., “3D Vision System for Vehicles,” Proceedings of the Intelligent Vehicles Symposium, IEEE, 2003.
- Frank, “Safety Rules the Road,” Design News, Oct. 11, 2004.
- Franke et al., “Autonomous driving approaches downtown”, Intelligent Systems and Their Applications, IEEE 13 (6), 40-48, Nov./Dec. 1999.
- Graefe et al., “A Novel Approach for the Detection of Vehicles on Freeways by Real-Time Visions,” Proceedings of the IEEE Intelligent Vehicles, Tokyo, Sep. 1996.
- Green, “Computer Image Processing—The Viking Experience,” ASA STI/Recon Technical Report A, Jul. 29, 1977, pp. 281-299.
- Greene et al., Creating Raster Omnimax Images from Multiple Perspective Views Using the Elliptical Weighted Average Filter, IEEE Computer Graphics and Applications, vol. 6, No. 6, pp. 21-27, Jun. 1986.
- Gregor, “A Vision System for Autonomous Ground Vehicles with a Wide Range of Maneuvering Capabilities,” International Conference on Computer Vision Systems, Springer-Verlag Berlin 2001.
- Gregor, “EMS-Vision: A Perceptual System for Autonomous Vehicles,” IEEE Transactions on Intelligent Transportation Systems, vol. 3, No. 1, Mar. 2002.
- Gregor, “EMS-Vision: Combining on- and off-road driving,” Aerospace/Defense Sensing, Simulation, and Controls. International Society for Optics and Photonics, 2001.
- Gregor, “EMS-Vision: Mission Performance on Road Vehicles,” Proceedings of the IEEE Intelligent Vehicles Symposium, Oct. 3-5, 2000.
- Halme et al., “Intelligent Autonomous Vehicles 1995,” International Federation of Automatic Control, Jun. 12-14, 1995, Part of Table of Contents.
- Hamit, “360-Degree Interactivity: New Video and Still Cameras Provide a Global Roaming Viewpoint”, Advanced Imaging, Mar. 1997, p. 50.
- Harris et al., “A Combined Corner and Edge Detector,” Alvey Vision Conference, vol. 15, 1988.
- Haywood, “Acquisition of a Micro Scale Photographic Survey Using an Autonomous Submersible,” Oceans '86, IEEE 1986.
- Hicks et al., “Panoramic Electronic Rear Vision for Automotive Applications,” SAE Technical Paper Series, International Congress and Exposition, Detroit, MI, Mar. 1-4, 1999.
- Hiramatsu et al., “Rearview Camera Based Parking Assist System with Voice Guidance,” Society of Automotive Engineers, Inc., SAE Technical Paper 2002-01-0759, 2002.
- Hock, “Wissensbasierte Fahrzengfurung mit Landmarken fur autonome Roboter,” Universitate der Bundeswehr, 1994.
- Hofmann, “EMS-Vision: Application to Hybrid Adaptive Cruise Control,” Proceedings of the IEEE Intelligent Vehicles Symposium, Dearborn, MI, Oct. 3-5, 2000.
- Hofmann, “Radar and Vision Data Fusion for Hybrid Adaptive Cruise Control on Highways,” Machine Vision and Applications, Springer-Verlag, 2003.
- Holt, “Integrale Multisensorielle Fahrumgebungserfassung nach dem 4D-Ansatz,” Universitat der Bundeswehr München, Nov. 12, 2004.
- Holt, “Tracking and Classification of Overtaking Vehicles on Autobahnen,” Proceedings of the Intelligent Vehicles Symposium, IEEE, 1994.
- Honda Worldwide, “Honda Announces a Full Model Change for the Inspire.” Jun. 18, 2003.
- Honda, “Honda Develops World's First Intelligent Night Vision System Able to Detect Pedestrians and Provide Driver Cautions,” Honda Worldwide, Aug. 24, 2004.
- IEEE 100—The Authoritative Dictionary of IEEE Standards Terms, 7th Ed. (2000).
- Impedovo, “Progress in Image Analysis and Processing III,” World Scientific, Capitolo, Monpoli, Italy, Sep. 22, 1993, Table of Contents.
- IPR Proceeding No. IPR2014-00221, filed Dec. 4, 2013, re U.S. Pat. No. 7,991,522.
- IPR Proceeding No. IPR2014-00222, filed Dec. 4, 2013, re U.S. Pat. No. 8,386,114.
- IPR Proceeding No. IPR2014-00223, filed Dec. 4, 2013, re U.S. Pat. No. 8,386,114.
- IPR Proceeding No. IPR2014-00227, filed Dec. 4, 2013, re U.S. Pat. No. 7,877,175.
- IPR Proceeding No. IPR2014-00228, filed Dec. 4, 2013, re U.S. Pat. No. 7,877,175.
- IPR Proceeding No. IPR2014-01204, filed Jul. 25, 2014, re U.S. Pat. No. 8,386,114.
- IPR Proceeding No. IPR2014-01206, filed Jul. 25, 2014, re U.S. Pat. No. 7,877,175.
- IPR Proceeding No. IPR2014-01208, filed Jul. 25, 2014, re U.S. Pat. No. 7,991,522.
- IPR Proceeding No. IPR2015-00960, filed Mar. 27, 2015, re U.S. Pat. No. 8,116,929.
- IPR Proceeding No. IPR2015-00961, filed Mar. 27, 2015, re U.S. Pat. No. 8,116,929.
- Ito, “A Real Time Distance Headway Measurement Method Using Stereo and Optical Flow,” Proceedings of the Intelligent Vehicles Symposium, ITTT, 1992.
- Japanese Article “Television Image Engineering Handbook, The Institute of Television Engineers of Japan”, Jan. 17, 1981.
- Johannes, “A New Microchip Ushers In Cheaper Digital Cameras”, The Wall Street Journal, Aug. 21, 1998, p. B1.
- Kan et al., “Model-based vehicle tracking from image sequences with an application to road surveillance,” Purdue University, XP000630885, vol. 35, No. 6, Jun. 1996.
- Kastrinaki et al., “A survey of video processing techniques for traffic applications”.
- Kinzel, “Pedestrian Recognition by Modelling Their Shapes and Movements,” Proc. of the 7th Intl. Conf. on Image Analysis and Processing, 1993.
- Kluge et al., “Representation and Recovery of Road Geometry in YARF,” Carnegie Mellon University, pp. 114-119.
- Kobe, Gerry, “Hypnotic Wizardry! (interior electronics),” Automotive Industries, vol. 169, No. 5, p. 60, May 1989.
- Koller et al., “Binocular Stereopsis and Lane Marker Flow for Vehicle Navigation: Lateral and Longitudinal Control,” University of California, Mar. 24, 1994.
- Koyamaishi et al., “Acquisition of Position and Direction of In-Vehicle Camera for HIR System,” IEEE Intelligent Vehicle Symposium, 2004.
- Kuhnert, “A vision system for real time road and object recognition for vehicle guidance,” in Proc. SPIE Mobile Robot Conf, Cambridge, MA, Oct. 1986, pp. 267-272.
- Kujawski, “Deciding the Behaviour of an Autonomous Mobile Road Vehicle,” IFAC Intelligent Autonomous Vehicles, Proceeding of the 2nd IFAC Conference on Intelligent Autonomous Vehicles, Finland, 1995.
- Kumar, “Representation of Scenes from Collections of Images,” Proceedings of IEEE Workshop on Representation of Visual Scenes, Jun. 24, 1995, pp. 10-17, Cambridge, MA.
- Labayrade et al., “Real Time Obstacle Detection in Stereovision on Non Flat Road Geometry Through V-disparity” Representation, Intelligent Vehicle Symposium, 2002. IEEE. vol. 2. IEEE, 2002.
- Liang, “Video Stabilization for a Camcorder Mounted on a Moving Vehicle,” IEEE Transactions on Vehicular Technology, vol. 53, No. 6, Nov. 2004.
- Lienert, “Fast Forward: Consepts: Tomorrow's Cars or Drives of Fancy,” Chicago Tribune, Feb. 11, 1996.
- Linkwitz, “High Precision Navigation 95,” University of Stuttgart, Apr. 1995.
- Linzmeler et al., “Pedestrian Detection with Thermopiles using an Occupancy Grid,” IEEE Intelligent Transportation Systems Conference, Washington, D.C., Oct. 3-6, 2004.
- Luo et al., “Pedestrian Detection in Near-Infrared Night Vision System,” IEEE Intelligent Vehicles Symposium, San Diego, CA, Jun. 21-24, 2010.
- Malik et al., “A Machine Vision Based System for Guiding Lane-change Maneuvers,” Sep. 1995.
- Masaki, “Vision-based Vehicle Guidance,” Proceedings of the 1992 International Conference on Power Electronics and Motion Control, IEEE, 1992.
- Maurer, “EMS-Vision: Knowledge Representation for Flexible Automation of Land Vehicles,” Proceedings of the IEEE Intelligent Vehicles Symposium, Dearborn, MI Oct. 3-5, 2000.
- Mei Chen et al., AURORA: A Vision-Based Roadway Departure Warning System, The Robotics Institute, Carnegie Mellon University, published Aug. 9, 1995.
- Miksch et al., “Automatic Extrinsic Camera Self-Calibration Based on Homography and Epipolar Geometry,”, IEEE, Intelligent Vehicles Symposium (IV), 2010.
- Minuro et al., “Functions and Devices of Mitsubishi Active Safety ASV,” IEEE, Jun. 1996.
- Miro et al., “Real-Time Image Stitching for Automotive 360° Vision Systems,” Audiovisual Engineering, Barcelona, Jul. 2014.
- Morgan et al., “Road edge tracking for robot road following: a real-time implementation,” vol. 8, No. 3, Aug. 1990.
- Mori et al., “Shadow and Rhythm as Sign Patterns of Obstacle Detection,” IEEE 1993.
- Murphy et al., “Application of Panospheric Imaging to a Teleoperated Lunar Rover,” Systems, Man and Cybernetics, Intelligent Systems for the 21st Century, IEEE, 1995.
- Nathan, Digital Video Data Handling, NASA JPL Tech Report 32-877, Pasadena, CA, Jan. 5, 1966.
- Nissan Press Release, “Highlights of Nissan's Exhibits at the 2001 Tokyo Motor Show,” Oct. 17, 2001, pp. 1-7.
- Paetzold, “Road Recognition in Urban Environment,” IEEE International Conference on Intelligent Vehicles, 1998.
- Parker (ed.), McGraw-Hill Dictionary of Scientific and Technical Terms Fifth Edition (1993).
- Pellkofer et al., “EMS-Vision: Gaze Control in Autonomous Vehicles,” Proceedings of the IEEE Intelligent Vehicles Symposium, Dearborn, MI, Oct. 3-5, 2000.
- Pellkofer et al., “Interaction of Perception and Gaze Control in Autonomous Vehicles,” Intelligent Robots and Computer Vision XX, Proceedings of SPIE, vol. 4572, 2001.
- Philomin et al., “Pedestrian Tracking from a Moving Vehicle”.
- Pomerleau et al., “Rapidly Adapting Machine Vision for Automated Vehicle Steering”, pp. 19-27, Apr. 30, 1996.
- Popular Science, “Pioneering Research Tools for Artificial Intelligence, Robot Insects,” Mar. 1991.
- Porter et al., “Compositing Digital Images,” Computer Graphics (Proc. Siggraph), vol. 18, No. 3, pp. 253-259, Jul. 1984.
- Pratt, “Digital Image Processing, Passage—ED.3”, John Wiley & Sons, US, Jan. 1, 2001, pp. 657-659, XP002529771.
- Rasmussen, “Combining Laser Range, Color, and Texture Cues for Autonomous Road Following,” IEEE Conference on Robotics for Automation, Washington, D.C., May 2002.
- Reexamination Control No. 90/007,519, dated Jun. 9, 2005, Reexamination of U.S. Pat. No. 6,222,447, issued to Schofield et al.
- Reexamination Control No. 90/007,520, dated Jun. 9, 2005, Reexamination of U.S. Pat. No. 5,949,331, issued to Schofield et al.
- Reexamination Control No. 90/011,477, dated Mar. 14, 2011, Reexamination of U.S. Pat. No. 5,949,331, issued to Schofield et al.
- Reexamination Control No. 90/011,478, dated Mar. 28, 2011, Reexamination of U.S. Pat. No. 6,222,447, issued to Schofield et al.
- Regensburger, “Object Classification for Obstacle Avoidance,” SPIE, vol. 1388 Mobile Robots V, 1990.
- Regensburger, “Zur Erkennung von Hindernissen in der Bahn elnes Stassenfahrzeungs durch maschinelles Echtzeitsehen,” Dec. 17, 1993.
- Regensburger, Uwe and Volker Graefe; “Visual Recognition of Obstacles on Roads”, 1994.
- Reichard et al., “A Real-time Approach to Traffic Situation Representation From Image Processing Data,” Proceedings of the Intelligent Vehicles Symposium 1995.
- Rembold, “Intelligent Autonomous Systems IAS-4,” Proceedings of the International Conference, Germany, Mar. 27-30, 1995.
- Reynaud et al., “On Board Data Fusion and Decision System Used for Obstacle Detection: 2D Vision Versus 1D Sensor Fusion,” Prometheus Prochip, Proceedings of the Intelligent Vehicles Symposium, IEEE, 1994.
- Rieder, “Multisensoriells Fahrzeugerkennung in einem verteilten Rechnersystem fur autonome Fahrzeuge,” Oct. 24, 2000.
- Risack et al., “Robust Lane Recognition Embedded in a Real-Time Driver Assistance System,” IEEE International Conference on Intelligent Vehicles, 1998.
- Rombaut, “Prolab 2: A Driving Assistance System,” Proceedings of the 1993 IEEE/Tsukuba International Workshop on Advanced Robotics, Japan, Nov. 8-9, 1993.
- Ross, “A Practical Stereo Vision System”, IEEE 1993.
- Sahli et al., “A Kalman Filter-Based Update Scheme for Road Following,” IAPR Workshop on Machine Vision Applications, pp. 5-9, Nov. 12-14, 1996.
- Saneyoshi, “3-D Image Recognition System for Drive Assistance,” Proceedings of the Intelligent Vehicles' 93 Symposium, 1993.
- Sasuki, “3-D Image Location Surveillance System for the Automotive Rear-View,” Vehicle Navigation & Information Systems Conference Proceedings, 1994.
- Schick, “Gleichzeitige Erkennung von Form and Bewegung durch Rechnersehen,” Uniersitat der Bundeswehr Munchen, Jun. 6, 1992.
- Schick, “Simultaneous Estimation of 3D Shape and Motion of Objects by Computer Vision,” Proceedings of the IEEE Workshop on Visual Motion IEEE, 1991.
- Schiehlen, “Kameraplattformen Fur Aktiv Sehande Fahrzeuge,” Verlag, No. 514, 1995.
- Schmid, “An Approach to Model-Based 3-D Recognition of Vehicles in Real-Time by Machine Vision,” Proceedings of the IEEE/RSJ/GI International Conference on Advanced Robotic Systems and the Real World, vol. 3, IEEE, 1994.
- Schmid, “Evaluation of Interest Point Detectors,” International Journal of computer vision 37.2, Dec. 22, 2010.
- Shum et al., “Panoramic Image Mosaics,” Microsoft Research Technical Report, 1997.
- Stenkula, “Vehicle Vicinity from Above: a Study of All-around Environment Displaying System for Heavy Vehicles,” Master of Science Thesis, Stockholm, Sweden, 2009.
- Sun et al., “On-road vehicle detection using optical sensors: a review”, IEEE Conference on Intelligent Transportation Systems, 2004.
- Szeliski, Image Mosaicing for Tele-Reality Applications, DEC Cambridge Research Laboratory, CRL 94/2, May 1994.
- The 35th Tokyo Motor Show, Nissan, IDEO Article, Oct. 2001.
- Thomanek et al., “Multiple object recognition and scene interpretation for autonomous road vehicle guidance” Oct. 1994.
- Thomanek et al., “Obstacle Detection, Tracking and State Estimation for Autonomous Road Vehicle Guidance,” Proceedings of the 1992 International Conference on Intelligent Robots and Systems, Raleigh, NC, Jul. 7-10, 1992.
- Thomanek, “Visuelle Erkennung and Zustandsschatzung von mehreren Strassenfahrzeugen zur autonomen Fahrzeungfuhrung,” VDI Verlag, No. 272, 1996.
- Thorpe et al., “The 1997 Automated Highway Free Agent Demonstration”, 1997 pp. 496-501, 1997.
- Thorpe, et al., Vision and Navigation for the Carnegie-Mellon Navlab, IEEE Paper 1988.
- Tokimaru et al., “CMOS Rear-View TV System with CCD Camera”, National Technical Report vol. 34, No. 3, pp. 329-336, Jun. 1988 (Japan).
- Toyota Crown Majesta Undergoes Complete Resign Article, 2004.
- Toyota Motor Corporation, “Present and future of safety technology development at Toyota.” 2004.
- Tsugawa et al., “An automobile with artificial intelligence,” in Proc. Sixth IJCAI, 1979.
- Tsugawa et al., “Vision-based vehicles in japan; machine vision systems and driving control systems”, IEEE Transactions on Industrial Electronics, vol. 41, No. 4, Aug. 1994.
- Turk et al., “Video Road-Following for the Autonomous Land Vehicle,” Proceedings of the IEEE International Conference on Robotics and Automation, vol. 4, 1987.
- Turk et al., “VITS-A Vision System for Autonomous Land Vehicle Navigation,” IEEE, 1988.
- Tzomakas and von Seelen, Vehicle Detection in Traffic Scenes Using Shadows, Internal report, Institut Für Neuroinformatik Bochum, Internal Report 98-06.
- Ulmer, “VITA II—active collision avoidance in real traffic” Proceedings of the Intelligent Vehicles '94 Symposium, Oct. 24-26, 1994.
- Van Leeuwen et al., “Motion Estimation with a Mobile Camera for Traffic Applications”, IEEE, US, vol. 1, Oct. 3, 2000, pp. 58-63.
- Van Leeuwen et al., “Motion Interpretation for In-Car Vision Systems”, IEEE, US, vol. 1, Sep. 30, 2002, p. 135-140.
- Van Leeuwen et al., “Real-Time Vehicle Tracking in Image Sequences”, IEEE, US, vol. 3, May 21, 2001, pp. 2049-2054, XP010547308.
- Van Leeuwen et al., “Requirements for Motion Estimation in Image Sequences for Traffic Applications”, IEEE, US, vol. 1, May 24, 1999, pp. 145-150, XP010340272.
- Van Leeuwen et al., “Requirements for Motion Estimation in Image Sequences for Traffic Applications”, IEEE, pp. 354-359, XP002529773, 2000.
- Vellacott, Oliver, “CMOS in Camera,” IEE Review, pp. 111-114 (May 1994).
- Visteon, “Innovations: Improving Nighttime Vision,” Oct. 18, 2001.
- Vlacic et al. (Eds), “Intelligent Vehicle Tecnologies, Theory and Applications”, Society of Automotive Engineers Inc., edited by SAE International, 2001.
- Wang et al., “CMOS Video Cameras”, article, 4 pages, University of Edinburgh, UK, 1991.
- Wang et al., CMOS Video Cameras, article, 1991, 4 pages, University of Edinburgh, UK.
- Wang, “Camera Calibration by Vanishing Lines for 3-D Computer Vision,” IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 13, No. 4, Apr. 1991.
- Williamson, “A High-Performance Vision System for Obstacle Detection,” Robotics Institute, Carnegie Mellon University, 1998.
- Wolberg, “A Two-Pass Mesh Warping Implementation of Morphing,” Dr. Dobb's Journal, No. 202, Jul. 1993.
- Wolberg, Digital Image Warping, IEEE Computer Society Press, 1990.
- Xiong, “Error Analysis of a Real-Time Stereo System,” Proceedings on the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1997.
- Yang, “Obstacle Detection in a Sequence of Rearview Images,” International Conference on Information, Communications and Signal Processing ICICS, Singapore, Sep. 9-12, 1997.
- Zhao, “Obstacle Detection by Vision for an Autonomous Vehicle,” Proceedings of the Intelligent Vehicles Symposium, 1993.
- Zheng et al., “An Adaptive System for Traffic Sign Recognition,” IEEE Proceedings of the Intelligent Vehicles '94 Symposium, pp. 165-170 (Oct. 1994).
- Zhu et al., “A Real-Time Vision System for Automatic Traffic Monitoring Based on 2D Spatio-Temporal Images,” Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision, 1996.
- Zhu et al., “Panoramic EPI Generation and Analysis of Video from a Moving Platform with Vibration,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, 1999.
Type: Grant
Filed: Apr 9, 2018
Date of Patent: Dec 17, 2019
Patent Publication Number: 20180225528
Assignee: MAGNA ELECTRONICS INC. (Auburn Hills, MI)
Inventor: Michael J. Higgins-Luthman (Livonia, MI)
Primary Examiner: Jaime Figueroa
Application Number: 15/948,058
International Classification: G06K 9/00 (20060101); H04N 7/18 (20060101); B60R 1/12 (20060101); B60Q 9/00 (20060101); H04N 5/232 (20060101); B60W 50/14 (20120101); B60R 1/00 (20060101); B60W 40/04 (20060101); H04N 5/374 (20110101);